biology phd job market

The job market for molecular biologists in the United States

There are currently an estimated 60,400 molecular biologists in the United States. The molecular biologist job market is expected to grow by 3.8% between 2022 and 2032.

How employable are molecular biologists?

CareerExplorer rates molecular biologists with a F employability rating, meaning this career should provide poor employment opportunities for the foreseeable future. Over the next 10 years, it is expected the US will need 7,900 molecular biologists. That number is based on 2,300 additional molecular biologists, and the retirement of 5,600 existing molecular biologists.

Are molecular biologists in demand?

The employment outlook and demand for molecular biologists is rather bleak. The principal reason behind this is that there is a glut of molecular and cell biology Ph,D.s in the market. This is especially true in the United States, the United Kingdom, and Australia. The current occupational environment means that to find jobs in their area of expertise, most of these Ph.D. holders will have to complete post-doctorate studies and publish high-impact papers. Such a commitment, while perhaps enticing from an educational perspective, imposes further financial burden, delays professional advancement, and confirms the high level of competition facing molecular biologists. The pharmaceutical industry, which has historically been a major employer and funder of research in the field, is experiencing layoffs and thereby exerting additional negative pressure on this career. These factors are forcing many aspiring molecular biologists to accept and remain for significant periods of time in roles related to, but beneath their completed level of study. Most opportunities tend to be in research laboratories with consulting firms and universities. Among the professional organizations and associations recommended to these biologists are the American Association for the Advancement of Science, American Society for Biochemistry and Molecular Biology, American Society of Cell Biology, Federation of American Societies for Experimental Biology, RNA Society, and Biophysical Society.

What’s the supply of molecular biologists?

The molecular biologist industry is concentrated in California, Massachusetts, Maryland

Molecular Biologist job market by state

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A sizzling biotech job market is streamlining the course to a career in chemistry

As phds leave postdocs early for industry, researchers are considering the future of their postdoc programs, by rick mullin, september 12, 2021 | a version of this story appeared in volume 99, issue 33.

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“What is happening with chemistry in America?” queries Matthew D. Hall, a biology group leader at the US National Center for Advancing Translational Sciences (NCATS) in a Twitter post July 8. “We’re losing postdocs to pharma after less than 1 year in our med chem labs. Is the job market that hot in chemistry?”

The answer, in short, is yes.

The combination of a healthy drug industry, heavy venture capital investment in start-ups, and new targets for small-molecule drugs is spurring a hiring spree, allowing some young PhD chemists, particularly in medicinal and computational chemistry, to skip the traditional years of postdoctoral training and go quickly to work in the pharmaceutical industry. The boom is great for chemists looking to start their careers, but it is raising questions about the role of the postdoc in the training of chemists today.

Related: Want a faculty position? Get ready to wait

Hall says he was prompted to start a conversation on Twitter after hearing multiple accounts of principal investigators in academic labs losing postdocs to industry. “And not in a scenario where they have done a 2- or 3-year postdoc, but 4 to 6 months in. I’m 44. I did a very long postdoc. I have a lot of friends who did two postdocs. That was not uncommon at all. Obviously, the opportunities are there now in a way they haven’t been for a while.”

Hall adds that it has become increasingly difficult to fill postdoc spots in his lab at NCATS. “I have chatted with a few people who applied for postdocs who basically said, ‘I’m not doing a postdoc. I have a job with pharma.’ ”

This is a troubling development, Hall says, given the traditional role of postdoctoral programs in training PhD-level chemists in medicinal chemistry.

Most PhD programs give a student a solid grounding in areas such as organic synthesis, mechanistic organic chemistry, and natural product chemistry, Hall says. “Medicinal chemistry requires you to acquire an understanding of how to work in a team, how to speak biology, how to speak modeling. How to be project-managed and how to look at and understand biological data that informs decisions that are made about the direction a chemistry program is going in. Some people get this at the PhD level, but not many.”

Joel P. Schneider, the chief of chemical biology at the National Cancer Institute (NCI), agrees that a postdoc fellowship provides a path from the classical chemistry learned during a PhD program to the practical realm of medicinal chemistry as conducted in industrial drug discovery. The NCI, which, like NCATS, is part of the US National Institutes of Health, provides an ideal setting, he says.

“When a fellow joins a particular lab at the NCI, that fellow is really joining all the labs at the NCI. There is a very fluid environment,” Schneider says.

While the labs at the NCI have not taken much of a hit, Schneider says, some fellows have moved into industry before their postdoc is over. He speaks of one former fellow who left for a job with a biotechnology company. She was subsequently hired by a firm involved with crop science, and then by a top 10 pharmaceutical company. “This happened in a year and a half,” he says.

Bill Wuest, a chemistry professor at Emory University, says the hot job market in pharmaceutical chemistry contrasts with a dearth of jobs in academia . And even some of those who have jobs in academia are making a move.

“I’ve seen quite a few people in the last 2 to 3 years leave tenured positions for industry jobs,” he says. The potential for a higher salary without having to juggle teaching remotely, keeping grants afloat, and caring for family during a pandemic has a lot of people looking at where their research aligns with industry, Wuest says.

Wuest says his thinking about postdocs has evolved in recent years. Traditionally, fellowships gave young chemists an experience in a new lab, preparing them for jobs, largely in academia, he says. It has become less clear what a postdoc offers a chemist who wants to pursue a job in industry.

Opportunities in biotech

Biotech companies are in the midst of a hiring surge, fueled by sizable rounds of venture capital financing, new contracts with Big Pharma partners, and initial public stock offerings. But much of their chemistry is outsourced, meaning that in-house chemists are involved at the design level and generally manage contract researchers who do the actual synthesis.

Some companies are looking for chemists with 3–5 years of industry experience. But this requirement doesn’t necessarily rule out recent graduates or postdoctoral fellows. Rachel Meyers, chief scientific officer at Faze Medicines , a Cambridge, Massachusetts–based start-up that is targeting biomolecular condensates with small molecules, questions the meaning of experience in areas of cutting-edge science.

“Some of the targets we are working on are not fully understood and have never been drugged before,” she says. “There are few people in our industry that have experience drugging biomolecular condensates.”

While the four full-time chemists at Faze had all worked in industry before joining the company, it is not requiring industry experience of new hires, Meyers says.

“Of course we are talking to the new folks all the time,” she says. “By definition, the person we need has to be unafraid to be at the cutting edge of interesting biology, to bring a really broad tool kit of ideas, strategy, and creativity to the problem of making drugs. Sometimes it’s the new folks who don’t know how to be afraid.”

Jnana Therapeutics , a 4-year-old Boston biotech company with a chemoproteomics platform for drug discovery, recently completed a round of financing and is looking to hire synthetic medicinal chemists this year and next.

“Last year we started in earnest to hire PhDs early in their career,” says Joel Barrish, cofounder and chief scientific officer at the company. “We hired a couple at the end of last year when we first noted the hot job market—that we had to be on the ball and use our networks to find really good-quality people, which we did.”

Barrish says the steady influx of venture capital into biotech is fueling job creation. “But I think it’s more than that,” he says. “I think there is a renaissance in small-molecule drug discovery.”

For others, a renaissance favors the highly skilled and most experienced. Joshua Horan, vice president of discovery chemistry at Nuvalent , a structure-based drug discovery specialist in Cambridge, Massachusetts, says the company, which outsources all its wet chemistry, has one position open, which requires 5 years of prior industry research.

“We are seeing a number of people still in postdocs applying for this position. I guess the hope is that the market is very hot, that they have a chance,” Horan says. “This is something I haven’t really seen before.”

He speculates that more surprises may be in store if the job market remains tight. “If there are not enough people compared to the positions available, I think industry would have no choice other than to lower requirements and train people. As of today, that is not the model, but it would not be a huge barrier if we had to do that.”

Contract development and manufacturing organizations (CDMOs) are also hiring at a record level. TCG GreenChem, a CDMO beginning operations at a site in New Jersey, has 32 chemists working in its labs and plans to grow the chemistry staff to 60 by year’s end.

“I’m receiving 400 to 500 résumés a week,” CEO Chris Senanayake says. “My group is mainly PhDs, but we are looking at people with master’s degrees and industry experience.”

Senanayake sees the CDMO sector as a good training ground for emerging chemists. “Our company is small enough for someone to learn the subject matter very fast,” he says. “In 3 years, they are at the level of 6 or 7 years at a pharmaceutical company.”

Turning pro

Luiza Bondila, who received her PhD in supramolecular chemistry in 2019 from the University of Oxford, says she was unsure what she wanted to do next but was leaning toward a career in academia. “As my PhD was nearing the end, I applied to a couple of postdocs in western Europe,” she says. “I had a couple of interviews but didn’t hear back.”

While waiting, she was contacted by a recruiter on behalf of Sterling Pharma Solutions, a CDMO in England. “I sent my CV and they invited me for an interview,” she says. “The response was positive, and they gave me an offer.”

Bondila says that when she weighed the advantages offered by industry, she liked what she saw as a better work-life balance than afforded by an academic position involving teaching and research. On the other hand, the deadlines in industry are tighter. Overall, she says her experience so far has disabused her of what she believes are common career-choice misconceptions among PhD students.

“I think a lot of young chemists don’t have a good enough overview of what industry means,” she says. Some view a job in industry as selling out. “They see it as not as much of an intellectual pursuit—going for the money for a job that is not as satisfying. But seeing it as less intellectual is a stretch. There are a lot of things involved in scaling up a process that you have to think about that you wouldn’t think about in academia.”

Justin Shapiro, who completed his PhD in organic chemistry at Washington University in St. Louis in 2017 before a postdoc at Emory, has also vacillated between academia and industry.

“When I first started my postdoc, I was pretty dead set on industry,” he says. “But early on I had some opportunities to get involved in teaching and mentorship as well as grant writing.” He enjoyed the academic sphere enough that he began considering it for a career.

Shapiro assembled his application materials and fired them off to a handful of universities. “It’s a long process,” he says. “And while I was waiting, a couple of industry interview opportunities came up. Before I knew it, I had industry offers in hand and had to think about what I wanted to do with my career. Ultimately, industry was the right route for me.”

He ended up taking a job at Circle Pharma, a macrocyclic peptide drug discovery company in San Francisco, where he works on solution-phase synthesis with about a dozen chemists. Several are PhDs, and some have master’s and bachelor’s degrees. Some came directly to work for Circle after finishing their degrees. Some had industry experience before joining the company.

“All my coworkers have been incredibly smart and nice and capable and have brought me on board very quickly,” Shapiro says. He says he’s not sure whether the unexpected number of industry job opportunities ultimately influenced his career choice, but as he landed interviews, he realized that he was headed where he wanted to be. “What I really wanted out of my career is to work in the drug discovery space, gain medicinal chemistry experience, and stay in the laboratory.”

Things also moved quickly for Josh Born, who received his PhD in organic synthesis from Purdue University in 2020 and now works as a research scientist at Eli Lilly and Company. In between, he spent a year in a postdoctoral fellowship at NCATS, leaving earlier than he’d planned.

Born says that as a classically trained synthetic chemist, he viewed his postdoc as an important step toward a job in the pharmaceutical industry. “I wasn’t necessarily privy to all the biological aspects of medicinal chemistry, such as assay development and how large an impact it can have on a small-molecule drug program,” he says.

“My goal was to get this kind of experience and use it in applying to the job market,” he says. “My timeline when I first got there was a typical postdoc, 2 to 4 years.”

Nonetheless, Born began hearing from recruiters and responding to job postings. He applied to the job at Lilly, which he saw listed online. It was outside his research field but in “an area I was very interested in,” he says. “I ended up hearing back rather quickly. Lilly was the career opportunity I was looking for the entire time.”

Related: Coronavirus dims chemistry job market prospects

Jackson Cahn received his PhD in chemistry-protein engineering from the California Institute of Technology in 2016. He finished a 3-year postdoc at the Swiss Federal Institute of Technology (ETH), Zurich, before taking a job with Merck & Co. in Rahway, New Jersey.

“I spent a year applying for academic jobs,” Cahn says, noting that the interview process was brutal. “I spent 2 months putting hundred-hour weeks into getting my job applications together while being a vaguely helpful member of the lab I was in. I got a few interviews.” Then the pandemic hit, causing a drag on an already slow-moving job search.

The pandemic didn’t seem to be slowing down the industry job market, however. “I applied for 52 faculty positions and got three interviews. I only sent out seven résumés to industry, and I got four interviews and three job offers,” he says.

Speed also characterizes work in an industrial lab, Cahn learned. “It’s a much faster time from having an idea to having it up and running in a lab,” he says. The timeline is accelerated by collaboration at a level new to him. “In academia, even if you are working with a team, you have your project, answerable to your principal investigator. Here, because I’m working with a team, people are waiting for me to deliver my part of the project. It’s been a learning curve in a good way, forcing me to learn new ways of thinking.”

The postdoc’s future

The flurry of hiring in the pharmaceutical sector has academia looking closely at the educational role of postdoctoral fellowships.

“There is absolutely still a place for the postdoc,” but it needs some attention, says Jen Heemstra, a chemistry professor at Emory and a C&EN columnist. “There is a huge gap between what the purpose of a postdoc should be and what postdocs end up being. That gap is created by things we need to change in our academic system and by imperfections in the economy. Hopefully this is a moment for us to have more dialogue in the community about the purpose of the postdoc and how we should align our practices in academia to meet the needs of early-career researchers.”

Hall at NCATS is concerned about the effect of PhD recipients’ skipping the postdoc. “I have this hang-up about chemists never really getting the right training experiences,” he says. “From a selfish point of view, it can impact some of the research programs we do in that if we can’t recruit postdocs, some programs might not ever move forward or take off.”

Hall says he is glad he raised a balloon on Twitter. “It gives you an idea of how really hot the market is right now for both medicinal chemists and computational chemists.” Which isn’t a bad thing, he says.

Related: Grappling with the decision to leave grad school

Schneider at the NCI agrees. “Keep in mind this is exactly what our field wants to see,” he says. “I love to see this type of job environment for young scientists. If we have postdocs that leave a little early, that’s just great. Let them start their careers, right?”

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A survey-based analysis of the academic job market

Jason d fernandes.

1 Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, United States

Sarvenaz Sarabipour

2 Institute for Computational Medicine, Johns Hopkins University, Baltimore, United States

3 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States

Christopher T Smith

4 Office of Postdoctoral Affairs, North Carolina State University Graduate School, Raleigh, United States

Natalie M Niemi

5 Morgridge Institute for Research, Madison, United States

6 Department of Biochemistry, University of Wisconsin-Madison, Madison, United States

Nafisa M Jadavji

7 Department of Biomedical Sciences Midwestern University, Glendale, United States

Ariangela J Kozik

8 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, United States

Alex S Holehouse

9 Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, United States

Vikas Pejaver

10 Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, United States

11 The eScience Institute, University of Washington, Seattle, United States

Orsolya Symmons

12 Department of Bioengineering, University of Pennsylvania, Philadelphia, United States

Alexandre W Bisson Filho

13 Department of Biology, Brandeis University, Waltham, United States

14 Rosenstiel Basic Medical Science Research Center, Brandeis University, Waltham, United States

Amanda Haage

15 Department of Biomedical Sciences, University of North Dakota, Grand Forks, United States

Associated Data

The authors confirm that, for approved reasons, access restrictions apply to the data underlying the findings. Raw data underlying this study cannot be made publicly available in order to safeguard participant anonymity and that of their organizations. Ethical approval for the project was granted on the basis that only aggregated data is provided (as has been provided in the supplementary tables) (with appropriate anonymization) as part of this publication.

Many postdoctoral researchers apply for faculty positions knowing relatively little about the hiring process or what is needed to secure a job offer. To address this lack of knowledge about the hiring process we conducted a survey of applicants for faculty positions: the survey ran between May 2018 and May 2019, and received 317 responses. We analyzed the responses to explore the interplay between various scholarly metrics and hiring outcomes. We concluded that, above a certain threshold, the benchmarks traditionally used to measure research success – including funding, number of publications or journals published in – were unable to completely differentiate applicants with and without job offers. Respondents also reported that the hiring process was unnecessarily stressful, time-consuming, and lacking in feedback, irrespective of outcome. Our findings suggest that there is considerable scope to improve the transparency of the hiring process.

Introduction

The number of PhDs awarded in science, technology, engineering and mathematics (STEM) has increased dramatically over the past three decades ( Cyranoski et al., 2011 ; Ghaffarzadegan et al., 2015 ), but the number of faculty positions available has essentially remained constant ( Schillebeeckx et al., 2013 ). In the US, for instance, the situation has not changed significantly since 2003, when the National Institutes of Health (NIH) received a major budget increase ( Alberts et al., 2014 ). Given the low numbers of faculty positions compared to the numbers of PhDs produced ( Larson et al., 2014 ; Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014 ), trainees are limited in their job prospects. Many also emerge from academic training feeling underprepared and under-mentored for any other type of job search ( McDowell et al., 2015 ). This leads to a high number of applicants per academic position, many of whom are uncertain about their chances of obtaining a faculty job ( Grinstein and Treister, 2018 ; Sauermann and Roach, 2016 ).

Cohorts of new PhDs are also both more diverse than before and more diverse than many current hiring committees ( Alberts et al., 2014 ; White, 2019 ; Bhalla, 2019 ). Scientific publishing is also faster-paced than it used to be: for example, evolutionary biologists recruited as "junior researchers" in 2013 had published nearly twice as many articles (22 ± 3.4) as those hired in 2005 (12.5 ± 2.4); the same study also found that the length of time between first publication and recruitment as a faculty member had increased from 3.25 (±0.6) to 8.0 (±1.7) years ( Brischoux and Angelier, 2015 ). Longer training periods have been reported repeatedly in many STEM fields, and are perceived as detrimental to both the greater scientific community and individuals in temporary postdoctoral positions ( Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014 ;  Ahmed, 2019 ; Rockey, 2012 ; Acton et al., 2019 ).

Despite these changes, the academic job search has largely remained the same, resulting in academic hiring being perceived as an opaque process with no clear standards or guidelines. Beyond a requirement for a doctoral degree and possibly postdoctoral training, faculty job advertisements rarely contain specific preferred qualifications. Furthermore, the criteria used to evaluate applicants are typically determined by a small departmental or institutional committee and are neither transparent nor made public. The amount of materials required for faculty job applications is also highly variable among hiring institutions, and often places a heavy burden on both applicants and search committees ( Lee, 2014 ).

Previous studies agree on a need to increase transparency in career outcomes and hiring practices ( Golde, 2019 ; Polka et al., 2015 ; Wright and Vanderford, 2017 ). The annual pool of faculty job applicants is large and provides a unique opportunity for examining the application process. We performed an anonymous survey, asking applicants for both common components of research and scholarly activity found on an academic CV, as well as information on their success through the 2018–2019 job cycle. We further performed a small-scale, complementary survey of search committee members. Here we present qualitative and quantitative data on the academic job market, including information on the number of successful off-site and on-site interviews, offers, rejections, and the lack of feedback.

Job applicants start by searching for relevant job postings on a variety of platforms ( Supplementary file 1 ). The initial electronic application generally consists of a cover letter addressing the search committee, a teaching philosophy statement, CV, and a research plan ( Figure 1 ). The length and content of these materials can vary drastically based on the application cycle, region, institution, or particular search committee. In the current system, the expectation is that application materials be tailored for each specific institution and/or department to which the applicant is applying. This includes department-specific cover letters ( Fox, 2018a ), but may also involve a range of changes to the research, teaching, and diversity statements.

The first column defines common terms in the academic job search; while the second column outlines how the search for an academic job progresses, from a job being posted to an offer being accepted.

The search committee convenes for a few meetings to shortlist the applicants. Applicants are then contacted for interviews somewhere between one to six months after application materials are due. Searches may include an initial off-site (remote) interview, followed by an on-site interview at the hiring university. The on-site interview typically lasts one or two days and consists of a research seminar, possibly a teaching demonstration, and likely a chalk-talk ( Rowland, 2016 ). The on-site interview also usually consists of one-on-one meetings with other faculty members, including a meeting with the hiring department chair, trainees, and the administrative staff.

After the interviews, candidates may be contacted and offered a position, usually in writing. The offer package will include the proposed start date, salary and start-up funds ( Macdonald, 2019 ). The time to offer is also variable, but is usually shorter than the time between application and first contact (based on anecdotal information). Importantly, a single search can result in multiple offers (for instance the department may be able to fund multiple competitive candidates, or the first-choice candidate may decline and the second candidate is given an offer). Searches can also fail if the committee does not find a suitable candidate for their program/department or "go dry" if the applicant(s) deemed qualified by the search committee decline their offer.

We designed a survey for early-career researchers aimed at bringing transparency to the academic job market (see Materials and methods and Supplementary file 41 ). The survey was distributed via Twitter, the Future PI Slack group, and email listservs of multiple postdoctoral associations, resulting in 322 responses from self-identified early-career researchers who applied for academic positions in the 2018–2019 application cycle. Of these, data from 317 respondents passed simple quality filters and were used for analyses. As all questions were optional, these 317 responses represent the maximum number in our analyses; in cases where respondents chose not to answer the question, we analyzed only the applicant subset with responses and list the number of responses used for each analysis in the appropriate figures and supplementary files.

Demographics of respondents

Respondents reported a large range in the number of submitted applications from a minimum of one to a maximum of 250 (median: 15). The respondent pool was notably enriched in applicants who received at least one off-site interview (70%), at least one on-site interview (78%) and at least one offer (58%); this may represent a significant bias towards successful applicants in our study, as a recent study shows that less than 23% of PhDs eventually secure a tenure-track position ( Langin, 2019 ).

Respondents represented researchers in a wide variety of fields, with 85% from life sciences and related fields, with relatively equal numbers of applications from men and women across this group ( Figure 2A ). Our survey captured data from an international applicant pool, representing 13 countries ( Figure 2B ). However, 72% of our respondents reported currently working in the United States, which may reflect the larger circulation of our survey on social media platforms and postdoctoral associations there. Most candidates applied to jobs within the United States (82%), Canada (33%), and the United Kingdom (24%). 96% of respondents entered the job market as postdoctoral researchers ( Figure 2C ). The applicants spent 1 to 13 years (median: 4 years) in a postdoctoral position. These data are consistent with a recent report suggesting that postdocs in the United States across a variety of fields spend an average of 2.5–3.6 years in their positions ( Andalib et al., 2018 ).

( A ) Distribution of survey respondents by self-identified gender and scientific field ( Supplementary file 2 ). Fields highlighted in green were grouped together as life-science related fields for subsequent analyses. ( B ) Distribution of countries where respondents were researching at the time of the survey (top, see Supplementary file 3 ) and the countries in which they applied to faculty jobs (green slices of pie charts, bottom; see Supplementary file 4 ). ( C ) Self-reported positions of applicants when applying for faculty jobs ( Supplementary file 5 ). ( D ) The number of years spent as a postdoctoral researcher ranges from 1 year or fewer (4% of applicants) to eight or more years (9% of applicants; maximum of 13 years, top). Life-science related postdoctoral training (n = 268 respondents) takes significantly longer than in other fields (n = 49 respondents; p=6.5×10 −6 , bottom; for data see Supplementary file 6 ; for statistical analysis see Supplementary file 7 ). ( E ) Number of postdoctoral positions held by survey applicants ( Supplementary file 8 ). ( F ) Median values for metrics of research productivity in the applicant pool ( Supplementary file 9 ).

Notably, in our survey population, postdocs in the life sciences spent a median of 5 years in a postdoctoral position, significantly longer than those in other fields, who reported a median postdoc length of 2.75 years prior to applying for a faculty position ( Figure 2D ), consistent with previous findings on increased training times in the life/biomedical sciences before junior faculty recruitment ( Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014 ; Brischoux and Angelier, 2015 ; Ahmed, 2019 ; Powell, 2017 ; Rockey, 2012 ). 68% of respondents went on the job market while in their first postdoctoral position ( Figure 2E ).

Applicants had a large range in their publication records, including number of papers co-authored, h-index, and total citation count. Respondents reported a median of 13 total publications (including co-authorships and lead authorships), with a median of 6 first author papers when entering the job market ( Figure 2F ).

Publishing metrics by gender

Gender bias in publishing and evaluation is well documented ( Aileen Day and Boyle, 2019 ; Centra and Gaubatz, 2000 ; Cameron et al., 2016 ; Witteman et al., 2019 ). The respondents to our survey were relatively evenly distributed across self-identified genders, with 51% identifying as male, 48% as female, and 1% preferring not to disclose this information (no applicants identified as non-binary; Figure 3A ). Men reported significantly more first-author publications, total publications, overall citations, and a higher h-index compared to women ( Figure 3B ); more men also reported being authors on papers in three journals with high impact factors (Cell, Nature and Science; Figure 3C ) than women. The gender differences we observe mirror those seen in other reports on differences in citation counts in STEM fields based on the corresponding author gender ( Schiermeier, 2019 ). Despite popular discussions on a need for papers in Cell, Nature, Science or other journals with a high impact factor ( Brock, 2019 ; McKiernan et al., 2019 ), 74% of respondents were not authors on a paper in Cell, Nature or Science (CNS), and a greater majority (~84%) did not have a first author publication in these journals ( Figure 3C ). Of the 51 respondents with papers in these journals, 49 (96%) were in a life science-related field, indicating that the valuation of these journals was highly field-specific ( Figure 3C ).

( A ) Distribution of gender (male, female, did not disclose) amongst survey respondents ( Supplementary file 2 , first row). ( B ) Publication metrics of survey respondents including number of first author papers (top), total publications (middle top), total citations (middle bottom), and h-index (bottom) for male and female respondents. Men in our survey reported more first-authored papers than women (medians of 7 and 5, respectively; p=1.4×10 −4 ), more total publications (medians of 16 and 11; p=3.0×10 −3 ), more overall citations (medians of 343 and 228; p=1.5×10 −2 ), and a statistically significant higher h-index (medians of 9.0 and 7.0; p=5.40×10 −3 ; see Supplementary files 7 and 9 ). ( C ) Although most applicants (83.6%) did not have first-author papers in CNS, those in the life sciences had more than applicants in other fields (p=0.012), and men had more than women (p=0.45; see Supplementary files 7 and 11 ). Note: CNS papers do not include papers in spin-off journals from Cell, Nature or Science. ( D ) Distribution of funding reported within training period (doctoral fellowship only in blue, postdoctoral fellowship only in red, fellowships during PhD and postdoc in purple, and no fellowship in gray). Females reported significantly more fellowship funding than males (42% of women vs 36% of men for predoctoral fellowships, and 72% of women, 58% of men for postdoctoral fellowships, p=2.40×10 −3 , χ 2  = 12.10, Chi-squared test, df = 2, see Supplementary files 7 and 13 ). ( E ) Preprints were posted by 148 of 270 (55%) individual candidates, with an average of 1.57 preprints reported per candidate (top). Number of preprints posted which were not yet accepted for journal publication (bottom) while applying for faculty jobs (see Supplementary file 14 ).

While 78% of respondents reported having obtained fellowships at some point in their career, this figure was 87% for women and 72% for men ( Figure 3D ). Women had better success at receiving both doctoral and postdoctoral fellowships. However, the questions in our survey did not distinguish between the types (e.g. government funded versus privately funded, full versus partial salary support) or number of fellowships applied to; many of these factors are likely critical in better understanding gender differences in fellowship support ( Figure 3D ).

Applications, interviews and offers

The 317 respondents submitted a total of 7644 job applications in the 2018–2019 application cycle, with a median of 15 applications per respondent ( Figure 4A ). Applicants were invited for a total of 805 off-site interviews (phone, Zoom or Skype; median: 1) and 832 onsite or campus interviews (median: 2), receiving 359 offers (median: 1; Figure 4A ). Although many hiring processes consist of an off-site (remote) interview, we found that this was not standard since the typical applicant received more on-site than off-site interviews. In our dataset, 42% of participants received no offers, 33% received one offer, 14% received two offers, 6% received three offers, and 6% received more than three offers. Candidates who received offers typically submitted more applications than those who received no offers, indicating that some candidates may not have submitted enough applications to have a reasonable chance of getting an offer ( Figure 4A,D ). According to a recent poll on Twitter (which received over 700 responses), most faculty received between one and three offers when they were applying for faculty positions ( Whitehead, 2019 ; Supplementary file 15 ).

( A ) Total and median numbers of applications, off-site interviews, on-site interviews and offers recorded in survey responses ( Supplementary file 19 ). ( B ) Correlations between the total number of applications submitted and off-site interviews (top; R 2  = 0.28), onsite interviews (middle) and offers (bottom; R 2  = 4.77×10 −2 ). ( C ) Correlations between the number of interviews completed and offers received (R 2  = 0.62). See Figure 4—figure supplement 1 for more details. ( D ) Total number of off-site interviews (top, p<4.10×10 −24 , on-site interviews (middle, p=1.20×10 −13 ) and offers (bottom, p=5.0×10 −5 ) for applicants who submitted at least 15 (the median) applications (in red) and less than 15 applications (in blue). ( E ) Fraction of applications that resulted in offers (offer percentages) for survey respondents who did not apply for jobs outside of faculty positions is significantly higher (p=2.0×10 −3 , Supplementary file 7 ) than for those who also applied for both academic and other types of jobs ( Supplementary file 14 ).

Figure 4—figure supplement 1.

Pearson correlation coefficient (R 2 ) between offer percentage total number of publications (top), number of first author publications (second graph), number of corresponding author publications (third graph), h-index (fourth graph), preprints posted (overall total, fifth graph; as well as those in which the peer-reviewed article was not published at the time of application, sixth graph), and number of patents filed, bottom graph). Yellow dots represent candidates with an offer, blue dots received no offers; black line represents linear best-fit and gray fill represents the 95% confidence interval for that fit. We examined several other publication metrics and found no correlation with the number of offers. Specifically, the total number of publications (R 2  = 8×10 −2 ), the number of first author (R 2  = 2×10 −2 ), the number of corresponding author publications (R 2  = 9×10 −4 ), and h-index (R 2  = 4×10 −3 ) did not significantly correlate with offer percentage.

Despite the fact that successful candidates submitted more applications, the number of applications per candidate did not correlate with the number of offers, while being only weakly correlated with the number of off-site interviews ( Figure 4B ). Not surprisingly, the number of on-site interviews strongly correlated with the number of offers received ( Figure 4C , bottom). Population medians changed slightly by gender as men submitted slightly more applications, but received slightly fewer off-site interviews. These small differences by gender were not statistically significant ( Figure 4A ). The median number of offers also did not vary by gender.

We split our population into two groups by application number, one group either at or below the median ( < 15 applications, n = 162) and the other group above the median (>15 applications, n = 155). These groups had a significant difference in success rates: respondents who submitted more than 15 applications had a significantly higher average number of off-site interviews ( Figure 4D ). We also asked whether respondents applied for non-faculty positions during this cycle ( Supplementary file 16 ). 71% of applicants did not apply for other jobs and these applicants had a small, but significant increase in offer percentage ( Figure 4E ).

Taken together, these data seemingly indicate that increasing the number of applications submitted can lead to more interviews, as suggested by others ( Jay et al., 2019 ), with the typical candidate submitting at least 15 applications to achieve one offer. However, the lower correlation between application number and offers (compared to application number and interviews) suggests that while higher application numbers can generate more interview opportunities, other criteria (e.g. the strength of the interview) are important in turning an interview into an offer.

Publication related metrics

The number of papers published, and the impact factors of the journals these papers were published in, can influence the chances of an early-career researcher obtaining an independent position ( van Dijk et al., 2014 ; Powdthavee et al., 2018 ). As mentioned previously, it is widely believed that you need a paper in Cell, Nature or Science to secure a faculty position in the life sciences ( McKiernan et al., 2019 ; Sheltzer and Smith, 2014 ; Fox, 2018b ). Our data demonstrates that a CNS paper is not essential to an applicant receiving a faculty job offer.

The majority (74%) of our respondents were not an author on a CNS paper ( Figure 5A ), and yet most participants received at least one offer (58%). However, applicants with a CNS paper did have a higher number of onsite interviews and faculty job offer percentage. Of our respondents, 16% were first author on a CNS paper, and these applicants had a significantly higher percentage of offers per application (p=1.50×10 −4 , median offer percentages: 11% with a CNS paper and 2% without a CNS paper) and on-site interviews (p=2.70×10 −4 , median onsite interview percentages: 21% with a CNS paper, and 10% without a CNS paper; Figure 5A ).

( A ) Pie charts show the fraction of candidates with authorship of any kind on a CNS paper (purple) versus those without (gray), and fraction of candidates who were first author on a CNS paper (purple) versus those who were not (gray). Distributions of off-site interviews (top; p=0.33), onsite interviews (middle; p=2.70×10 −4 ) and offers (bottom; p=1.50×10 −4 ) for applicants without a first-author paper in CNS (gray), and those with one or more first-author papers in CNS (purple; Supplementary files 11 , 12 , 17 ). ( B ) Significant associations were found between offer percentage and the number of first-author papers in CNS (top panel, p=1.70×10 −3 ), career transition awards (second panel, p=2.50×10 −2 ), total citations (third panel, p=2.92×10 −2 ), and years on the job market (fourth panel, p=3.45×10 −2 ). No significant associations were found between offer percentage and having a postdoc fellowship (fifth panel), being above the median in the total number of publications (sixth panel), being an author in any position on a CNS paper (seventh panel), h-index (eighth panel), years as a postdoc (ninth panel), number of first-author papers (tenth panel), number of patents (eleventh panel), or graduate school fellowship status (twelfth panel; Supplementary files 6 , 7 , 9 , 10 , 11 , 12 , 13 and 21 ). ( C ) The plots show total citations for those without an offer (blue) and those with one or more offers (gold), for all applicants with one or more first-author papers in CNS (top left); for all applicants without a first-author paper on CNS (bottom left); for all applicants with independent funding (top right); and for all applicants without independent funding (bottom right). In two cases the p value is below 0.05. The bar charts show the offer percentages (gold) for the four possible combinations of career award (yes or no) and first-author paper in CNS (yes or no): for applicants with a first-author paper in CNS, p=0.56, χ 2  = 0.34; for applications without, p=0.17, χ 2  = 1.92). ( D ) Summary of significant results testing criteria associated with offer outcomes through Wilcoxon analyses ( Supplementary file 7 ) or logistic regression ( Supplementary file 24 ).

Figure 5—figure supplement 1.

We performed identical analysis as in Figure 5 but restricted to applicants (n = 269) who described their field as life-science related (as defined in Figure 2 ).

Figure 5—figure supplement 2.

Each rounded node represents an independent variable and each rectangular node represents one of two possible outcomes (offer (gold) or no offer (blue)). Only those variables in Figure 5B were included. In the case of binary variables such as funding and fellowships, ">0" indicates a "yes" and "<=0" indicates a "no". All other variables, except for h-index, were split based on counts. The outcome nodes are labeled with three pieces of information: ( Cyranoski et al., 2011 ) the number of applicants who fell into the given branch ( n ), ( Ghaffarzadegan et al., 2015 ) the most common outcome in that branch, and ( Schillebeeckx et al., 2013 ) the fraction of individuals with that outcome. For example, the rightmost branch shows applicants who had a career transition award and h-index >4. They constitute the largest group in our dataset (61 individuals). However, only 77% of these applicants received an offer. Similarly, the second and third largest groups included 51 applicants (63% with offer) and 42 applicants (67% with offer) respectively (see eighth outcome box from right and leftmost box). These three groups accounted for 48.6% of our survey respondents. Note that while decision trees have often been used as prediction models, this tree is only reflective of our dataset and choice of algorithm and parameters. We have used this solely for visualization purposes and advise against using this prospectively to evaluate chances of success on the job market as there may be alternative trees that are equally plausible and accurate. In fact, the accuracy of the overall decision tree in distinguishing between candidates with offers and those without was only 58.5%. Furthermore, no group with more than two applicants consisted purely of those with offers and those without. Even in the nine groups where the most common outcome was "no offer", on average, 25% of the applicants did receive offers.

Since the number of on-site interviews and offers are highly correlated ( Figure 4C ), it is unclear if this increased success simply represents a higher chance at landing more onsite interviews. It is important to note that this effect is correlative and these candidates likely had other attributes that made them appealing to the search committee(s).

We examined several other publication metrics and found no correlation with the number of offers. Specifically, the total number of publications, the number of first author publications, the number of corresponding author publications, and h-index did not significantly correlate with offer percentage ( Figure 4—figure supplement 1 ). When we separated candidates who were above and below the medians for each of these metrics and compared the distribution of offer percentages, only the total number of citations significantly associated with a higher offer percentage ( Figure 5B ). Although the offer percentage was generally higher for applicants above the median for the other metrics, none of these differences were statistically significant ( Figure 5B ).

Preprints, or manuscripts submitted to an open-access server prior to peer-reviewed publication, are becoming increasingly popular among early-career researchers ( Sever et al., 2019 ), particularly in the life sciences, and can boost article citations and mentions ( Sarabipour et al., 2019 ; Fraser et al., 2019 ; Abdill and Blekhman, 2019 ; Conroy, 2019 ; Fu and Hughey, 2019 ).

We received 270 applicant responses on the use of preprints; 55% of respondents had posted at least one preprint, and 20% had posted between two and six preprints ( Figure 3E , top). At the time of faculty job application, 40% of these respondents had an active preprint that was not yet published in a journal ( Figure 3E , bottom), with an average of 0.69 active preprints per person. A number of candidates commented that preprinted research was enormously helpful and served to demonstrate productivity before their paper was published ( Supplementary files 17 and 18 ).

Fellowships and career transition awards

Respondents were highly successful in obtaining fellowship funding during their training (80% received a fellowship of any kind, Figure 3D ). Applicants with a postdoctoral fellowship had a greater offer percentage than those without, although the effect was not significant after correcting for multiple comparisons (p=0.17); doctoral fellowships did not appear to influence offer percentage ( Figure 5B ).

Receiving funding as an early-career researcher is part of a favorable research track record ( Eastlack, 2017 ). A recent study of publicly available data indicates that the proportion of faculty receiving their first large research program grant (an R01 through the NIH) with a history of funding as a trainee (F and K awards through the NIH) is significantly increasing, driven mostly by K awards. Pickett states: "While not a prerequisite, a clear shift is underway that favors biomedical faculty candidates with at least one prior training award" ( Pickett, 2019 ).

Our survey differentiated the types of funding a trainee can receive into predoctoral and postdoctoral fellowships (discussed above), and career transition awards, for which the trainee is listed as the PI and funds can often transition with the trainee to a hiring institute (e.g. the Burroughs Wellcome Fund Career Awards at the Scientific Interface or the NIH K99/R00 Pathway to Independence award). Career transition awards were less frequent, with 25% of respondents receiving awards on which they were PI/co-PI ( Supplementary file 20 ). Respondents with transition funding received a higher percentage of offers ( Figure 5B ).

Patents are considered positive metrics of research track record, although their importance and frequency can vary between fields. Only 19% of applicants reported having one or more patents on file from their work when entering the job market ( Supplementary file 21 ). The number of patents held by the applicant did not correlate with the number of offers received ( Figure 4—figure supplement 1 ) and the percentage of offers did not change between those with or without a patent ( Figure 5B ).

Years on the job market

We also asked how many application cycles they had been involved in. Approximately 55% of our respondents were applying for the first time, and these candidates fared significantly better in terms of offer percentages than those who were applying again ( Figure 5B ). Additionally, a number of applicants took advantage of resources that provided information about the job application process ( Supplementary file 22 ), and those that did found them helpful ( Supplementary file 23 ).

Analyses such as the work presented here may help applicants refine and present their materials and track record in a manner that might improve success and decrease repeated failed cycles for applicants.

Interplay between metrics

We next examined the relationship between each of the traditional criteria that were significantly associated with an increase in offer percentage. The criteria included being first author on a CNS paper, total citations, and career transition awards.

Overall, we had 241 applicants that fully responded to all of our questions about these metrics. Pairwise testing of each of these criteria found no statistically significant relationships between variables (p=0.45, career transition awards vs CNS; p=0.26 total citations vs CNS; p=0.29 career transition awards versus total citations). Regardless, we plotted subgroups based on offer status and each of these criteria to see if there was evidence for any general trends in our dataset ( Figure 5C ). Notably, respondents who were first author on a CNS paper and received at least one offer had a greater number of total citations than those who were first author on a CNS paper but did not receive any job offers. Applicants who were first author on a CNS paper or who had a career transition award had higher percentages of securing at least one offer, and those with both had an even greater percentage, although the differences between these groups was not statistically significant.

This analysis suggests that the combination of different criteria holistically influence the ability to obtain an offer. Therefore, we performed logistic regression to examine the relationship between multiple variables/metrics on the successful application outcome of receiving an offer on a subset of applicants (n = 105) who provided answers across all variables. We implemented a rigorous variable selection procedure to maximize accuracy and remove highly correlated variables. This resulted in a model that included only seven variables ( Supplementary file 24 ).

This regression model revealed that a higher number of applications, a higher citation count and obtaining a postdoctoral fellowship were significantly associated with receipt of an offer. When missing values were imputed and the full applicant pool (n = 317) was considered, all previous variables remained significant, and a significant positive coefficient was also observed for having a career transition award. In both versions of the model, the search for non-academic jobs was significantly negatively associated with offer status ( Figure 5D ). We note that the model with imputed data was more accurate than that with missing values excluded at distinguishing between applicants with and without offers in 10-fold cross-validation experiments. However this accuracy was found to only be 69.6%, which is insufficient to construct a usable classifier of offer status. Due to the predominance of applicants from the life sciences in our dataset, we also repeated these analyses on a subset containing only these applicants. While more variables were included in the model, the general trends remained the same, with the addition of the number of years spent on the job market as a significant negative factor in receiving an offer ( Figure 5—figure supplement 1 ; Supplementary file 25 ).

Finally, we extended this analysis to visualize the interplay between all variables in Figure 5B by learning a decision tree automatically from the collected data ( Figure 5—figure supplement 2 ). The algorithm tries to partition the applicants into groups such that each group is entirely composed of individuals with at least one offer or without. A variety of different classifier groups were identified, but no group contained more than ~19% (61 out of 317) of the dataset. In fact, the accuracy of the overall decision tree in distinguishing between candidates with offers and those without was only ~59% ( Figure 5—figure supplement 2 ).

Taken together, these results suggest that there are multiple paths to an offer and that the variables we collected do not sufficiently capture this variability.

Levels of teaching experience

Discussions surrounding the academic job market often center on publications and/or funding, while teaching experience generally receives much less attention. However, the level of teaching experience expected from the applicants can vary, but mostly depends on the type of hiring institution.

We asked applicants whether they focused their applications to a specific type of institution (R1, PUI, or both; see Box 1 for definitions), allowing us to examine teaching experience across R1 and/or PUI applicants. Most respondents applied to jobs at R1 institutions ( Figure 6A ), which may explain the focus on research-centric qualifications. It remains unclear what the emphasis on teaching experience is for search committees at R1 institutions, however the literature suggests that there seems to be a minimal focus ( Clement et al., 2019 ). Additionally, there might be differences in departmental or institutional requirements that are unknown to outsiders. What is commonly accepted is that many applications to an R1 institution require a teaching philosophy statement.

( A ) Distribution of institution types targeted by survey applicants for faculty positions (PUI only in blue, R1 institutions only in green, or both in red, Supplementary file 26 ). ( B ) Distribution of teaching experience reported by applicants as having TA only experience (in purple), beyond TA experience (e.g. teaching certificate, undergraduate and/or graduate course instructorship, guest lectureship and college adjunct teaching, (in orange), or no teaching credentials (in green; Supplementary files 27 and 28 ). ( C ) Distribution of teaching experience (TA experience, right, vs. Beyond TA experience, left) for applicants who applied to R1 institutions only (in green), PU institutions only (blue), or both R1 and PUIs (in red), ( Supplementary file 27 ). The degree of teaching experience did not change based on the target institution of the applicant (p=0.56 (ns), χ 2  = 0.41; Chi-squared test). ( D ) Association between offer percentage and teaching experience is not significant (p=0.16; Supplementary files 7 , 27 and 28 ).

Definition of specific terms used in this study.

Early-career researcher (ECR): For the purpose of this study, we define an ECR to be anyone engaged in research who is not recognized as an independent leader/investigator of a research group. This includes graduate and postdoctoral researchers; junior research assistants, research associates, and staff scientists.

Principal Investigator (PI): A scholar recognized as an independent leader of a research group. This includes full professors, group leaders, and tenure-track, non-tenure-track or tenured faculty.

Faculty Job Applicant: An early-career researcher with a PhD (a recent graduate, postdoctoral fellow or research scientist) who seeks to apply for a PI position (see above), usually at the assistant professorship level.

STEM Fields: STEM is an acronym for degrees in fields related to science, technology, engineering, and mathematics. STEM graduates work in a wide variety of fields including the life sciences, the physical sciences, different areas of engineering, mathematics, statistics, psychology, and computer science.

Research Mentor: A research advisor, usually the PI of a lab who mentors graduate and postdoctoral researchers during their academic training in his/her lab.

Adjunct Lecturer: A teacher or post-PhD scholar who teaches on a limited-term contract, often for one semester at a time. This individual is ineligible for tenure.

Teaching Assistant (TA): An individual who assists a course instructor with teaching-related duties in a lecture-based and/or laboratory-based undergraduate or graduate level course.

Doctoral/Graduate and Postdoctoral Fellowships : Funding mechanisms to support the training of a graduate or postdoctoral researcher: the proposal for this is written by the trainee and contains a mentoring/training plan and request for funding to support the trainee salary and/or part of their research expenses such as equipment, lab supplies and travel expenses typically for 1–3 years.

Career Transition Awards: Funding mechanisms facilitating senior trainees towards independent research careers: Includes core/substantial funds to fully support 1–3 years of postdoctoral salary and additional 2–5 years of independent faculty research and staff salaries as well as support for research expenses such as equipment, lab supplies and travel expenses. As a result, some portion of these funds can transition from the training institute to the hiring institute.

R1 University: There are 131 institutions in the United States that are classified as "R1: Doctoral Universities – very high research activity" in the Carnegie Classification of Institutions of Higher Education (2019 update), can be private or public.

R2 University: There are 135 institutions in the United States that are classified as “R2: Doctoral Universities – high research activity" in the Carnegie Classification of Institutions of Higher Education (2019 update), can be private or public.

R3 University, PUI or Small Liberal Arts College (SLAC): Primarily undergraduate institutions (PUI) are often smaller than large research universities, can be private or public, and offer varying levels of resources for students and faculty. Many faculty at PUIs run a research lab while maintaining significant teaching loads and heavy contact hours with students.

Almost all respondents (99%) had teaching experience ( Figure 6B ): for roughly half this experience was limited to serving as a Teaching Assistant (TA; Box 1 ), with the rest reporting experience beyond a TA position, such as serving as an instructor of record ( Figure 6B ). The degree of teaching experience did not change based on the target institution of the applicant ( Figure 6C ), nor did the percentage of offers received significantly differ between groups based on teaching experience ( Figure 6D ).

Research versus teaching-intensive institutions

To our knowledge, there is a lack of systematic evidence describing the process or expected qualifications of a PUI-focused ( Box 1 ) job search ( Ramirez, 2016 ). A subgroup of 25 "PUI Focused" applicants responded to our survey, and, despite this small number, we aimed to describe this important sub-group relative to "R1 Focused" applicants as well as applicants who applied to both types of institutes. The PUI subgroup included a majority of female applicants (60%, Figure 7A ) while the R1 subgroup had a majority of male applicants (54%, Figure 7A ). Within the PUI subgroup, no differences were seen in the number of first author publications across genders ( Figure 7B ), although women had a better fellowship history ( Figure 7C ). The median number of remote interviews, onsite interviews, and offers was also similar to that for the R1 subgroup, although the PUI subgroup submitted fewer applications ( Figure 7E ). Although both subgroups reported teaching experience ( Figure 7D ), the PUI subgroup was enriched in adjunct, visiting professor, instructor of record, community college, or contract-based teaching experiences ( Figure 7F ). Having adjunct experience did not significantly increase the median number of offers received for applicants focused on PUIs, R1s, or both types of institutions ( Figure 7G ).

( A ) The gender distribution applicants who focused on applying to PUIs ( Supplementary file 26 ). ( B ) The gender distribution and number of first-author publications of the applicant who focused on applying to PUIs (p=0.88). ( C ) Summary of the fellowship history by gender for PUI focused applicants ( Supplementary file 13 ). ( D ) Distribution of teaching experience of PUI focused applicants ( Supplementary file 27 ). ( E ) The median number of applications, off-site interviews, on-site interviews and offers for PUI focused applicants. ( F ) Percentage of survey respondents who identified having "adjunct teaching" experience ( Figure 1 ) based on target institution (p=5.0×10 −4 ; χ 2  = 27.5, Chi-squared test). ( G ) The number of offers received segregated by "adjunct teaching" experience in either PUI focused applicants (p=0.55) or R1/both R1 and PUI focused applicants (p=0.98).

A time-consuming and opaque process with little feedback

We asked the applicants to comment on whether any aspect of their training or career was particularly helpful or harmful to their faculty applications ( Figure 8A–B ). We used word clouds ( Supplementary files 27 and 28 ) to analyze recurrent themes in these open-ended questions. The applicants identified funding as most helpful for their applications, and no-funding as subsequently harmful; this perception agrees with the data presented above ( Figure 8A , Figure 5C , Figure 4—figure supplement 1 ). Additionally, perceptions were also in line with the rest of the data, in that they were unable to largely agree on other measurable aspects of their career that were perceived as helpful. Qualitative aspects that were perceived as particularly helpful included networking and attending/presenting at conferences. Interestingly interdisciplinary-research, which is often highlighted as a strength and encouraged by institutions and funders, was perceived by candidates as a challenge to overcome. Indeed, interdisciplinary candidates may pose an evaluation challenge for committees, given the differences in valuation of research metrics across fields, the extended training time required to master techniques and concepts in multiple fields, as well as valuation of interdisciplinary teams of specialists over interdisciplinary individuals ( Eddy, 2005 ).

Three word clouds summarizing qualitative responses from the job applicant survey respondents to the following questions: A ) "What was helpful for your application? " (top; Supplementary file 17 ), ( B ) "What was an obstacle for your application? " (middle; Supplementary file 18 ), and C ) "What is your general perception of the entire application process?" (bottom; Supplementary file 31 ). The size of the word (or short phrase) reflects its frequency in responses (bigger word corresponds to more frequency). Survey respondents were able to provide longer answers to these questions, as shown in Supplementary files 17 , 18 and 31 . 'CNS-papers' refers to papers in Cell, Nature or Science; 'Pedigree' refers to the applicant’s postdoc lab pedigree or postdoc university pedigree; 'Grant-Writing' refers to the applicant’s grant writing experience with their PhD or postdoctoral mentor; 'Peer-reviewing' refers to the experience of performing peer-reviewing for journals; 'Interdisciplinary-research' refers to comments stating that Interdisciplinary research was underappreciated; 'two-body problem' refers to the challenges that life-partners face when seeking employment in the same vicinity; 'No-Feedback' refers to lack of any feedback from the search committees on the status, quality or outcome of applications.

Notably, many applicants found the amount of time spent on applications and the subsequent lack of feedback from searches frustrating ( Figure 8B–C ). Most applicants never received any communication regarding their various submissions. For instance, an applicant who applied for 250 positions only received 30 rejections. Overall, our respondents submitted 7644 applications ( Figure 4A ) and did not hear anything back in 4365 cases (57% of applications), receiving 2920 formal rejection messages (38% of applications; Supplementary file 19 ). Application rejection messages (if received at all) most often do not include any sort of feedback. Additionally, a considerable amount of time is spent on writing each application and extensive tailoring is expected for competitive materials. Combining these insights, it is therefore unsurprising that almost all applicants, including applicants that received at least one offer ( Supplementary file 29 ), found the process "time-consuming", a "burden on research", and "stressful" ( Figure 8B–C ).

44% of respondents had applied for faculty jobs for more than one cycle ( Supplementary file 30 ). Though applicants who applied for more than one cycle had significantly lower offer percentages (p=3.45×10 −2 ; Figure 5B ), many reported perceived benefits from significant feedback from their current PI through their previous application cycles. Though mentorship was not as often reported as specifically helpful ( Supplementary file 17 ), the lack of mentorship was a commonly cited harmful obstacle ( Figure 8B , Supplementary file 18 ). Lastly, multiple candidates felt that issues pertaining to family, the two-body problem (need for spousal/significant other hire), parental leave, or citizenship status significantly harmed their prospects.

The view from the search committees

To learn more about the characteristics search committees valued in applicants, we performed an exploratory survey of members of such committees. This anonymous survey was distributed in a limited fashion, taking advantage of the professional networks of the authors. Fifteen faculty members responded, with nine having been involved in search committees for over ten years ( Figure 9A ). As with our survey of applicants, we focused on faculty members at R1 academic centers working in life sciences (14/15 of those polled) and engineering (1/15) within the United States ( Figure 9A ).

Search committee members were asked on how specific factors were weighted in the decision on which applicant to extend an offer to ( Supplementary files 33 – 38 ). All search committee members surveyed were based at R1 universities ( Box 1 ). ( A ) Distribution of the fields of study and years of experience for the search committee survey respondents. ( B ) The median number of faculty job openings, number of applicants per opening, applicants that make the first cut, applicants who are invited for phone/Skype interviews, and offers made. ( C ) The quantitative rating of search committee faculty on metrics: candidate/applicant research proposal, career transition awards, postdoctoral fellowships, graduate fellowships, PI/mentor reputation (lab pedigree), Cell/Nature/Science journal publications, Impact factor of other journal publications, Teaching experience and value of preprints based on a 5-level Likert scale where 1 = not at all and 5 = heavily. ( D ) Visual summary of the job applicant perception (from word cloud data) and the results of both surveys (statistical analyses of the applicant survey and criteria weighting from the search committee survey). A number of metrics mentioned in short answer responses were not measured/surveyed across all categories. These missing values are shown in gray.

Two-thirds of respondents replied that the search committees they sat on typically received over 200 applicants per job posting, with one-third receiving 100–199 applications per cycle. Between 5 and 8 applicants were typically invited to interview on-site; one-third of respondents replied that off-site interviews (e.g., via phone or Skype) were not performed ( Figure 9B ). These statistics help demonstrate the challenges that hiring committees face; the sheer volume of applicants is overwhelming, as mentioned explicitly by several search committee respondents ( Supplementary file 32 ).

We asked what factors search committee members found most important, what their perception of the market was, and how they felt it had changed since they first became involved in hiring. We also asked them to weigh specific application criteria in evaluating an application from 1 (not weighted at all) to 5 (heavily weighted; Figure 9C ). Criteria such as transition awards were consistently ranked highly, matching applicant perception; however, committee members also placed substantial emphasis on the research proposal. Two-thirds viewed preprints favorably, although their strength may not yet be equivalent to published peer-reviewed work ( Figure 9C ). In follow-up questions, a number of respondents emphasized that the future potential of the candidate both as a colleague and a scientist was important.

Since this last point was not prominent in our survey of job applicants, we looked for discrepancies in the two sets of responses ( Figure 9D ). In general, search committees placed greater emphasis on the future potential and scientific character (research proposal, research impact, collegiality), while applicants focused on publication metrics and funding. However, despite the search committees placing less emphasis on papers in CNS, candidates with papers in these journals were more successful.

We also asked if there were additional factors that search committees wished applicants knew when applying ( Figure 10 ). Several emphasized the quality of the research and papers was the most important factor for assessing prior achievement, but added that a compelling and coherent research proposal was also critical, and was sometimes underdeveloped in otherwise competitive candidates. The importance of departmental fit was also emphasized; interpersonal interactions with faculty members at the interview stage were also mentioned. This last sentiment is consistent with a recent Twitter poll which found that "overall attitude/vibe" was the single most important factor for selection at the interview stage ( Tye, 2019 ). Intriguingly, while one faculty respondent noted that they rarely interview any applicant without a career transition award, such as a K99/R00 Pathway to Independence Award from the NIH (a situation they noted as problematic), another lamented that applicants worried too much about metrics/benchmarks anecdotally perceived to be important, such as receiving these awards. Finally, a majority of respondents noted that it was easy to identify good candidates from their submitted application (11/15), that there were too many good applicants (10/15), and that candidates often underperformed at the interview stage (10/15) ( Figure 10 , Figure 10—figure supplement 1 , Supplementary File 35 ).

Two word clouds representing responses from members of search committees in response to the following questions: A) "What information do you wish more candidates knew when they submit their application?", and B) "Have you noticed any changes in the search process since the first search you were involved in?" The size of the word/phrase reflects its frequency in responses, with larger phrases corresponding to more frequent responses. Search committee faculty members were able to provide long answers to both questions ( Supplementary files 38 and 39 ).

Figure 10—figure supplement 1.

Bar chart showing the number of search committee respondents who held each of the opinions shown for candidates applying to academic jobs that they had been in the search committees for ( Supplementary file 36 ). Additional files.

Challenges in the academic job market

Currently, there is little systematic evidence for what makes a competitive faculty candidate. As with any opaque, high-pressure environment, an absence of clear guidelines and expectations coupled with anecdotal advice can lead individuals to focus on tangible goals and metrics that they feel will help them stand out in the system. Our findings were consistent with several commonly held notions: the number of applications submitted, career transition awards (e.g. a K99/R00 award), and total citation counts were significantly associated with obtaining offers in our Wilcoxon test and when jointly considering all variables in a logistic regression analysis. Joint academic/industry job searches were negatively associated with obtaining academic offers in both analyses, while the number of years an applicant was on the job market was negatively associated in our Wilcoxon analysis. Papers in CNS were only significantly associated with offers in the Wilcoxon analysis, while postdoc fellowships were only significant in the logistic regression.

Metrics such as career transition awards and postdoctoral fellowships can be broadly categorized as funding metrics and the positive association between these metrics and offer outcomes likely reflects the hiring institute being confident that the candidate will be competitive for future funding for their research program. Indeed, career transition awards essentially provide additional start up funds, while postdoc fellowships provide a track record of funding. Although postdoc fellowships were not significant in our Wilcoxon analyses, this metric was significant in our life science-specific Wilcoxon subgroup analysis ( Figure 5—figure supplement 1 .) as well as our logistic regression on the whole dataset ( Figure 5D ). The search committee respondents confirmed the benefit of career transition funding as major strengths for an application.

Association between offers and the number of applications, non-academic job searches, and years on the academic job market requires cautious interpretation. Given that receiving any single faculty offer is a low-probability event, there is value in submitting enough applications to increase the odds of receiving an offer. However, there is likely a balance in ensuring the quality of each application, which requires time and effort to individually tailor to each position. Searching for non-academic jobs might detract from the time available to tailor applications, although the negative association may also reflect other factors such as the typically swifter non-academic hiring timeline, which could cause applicants to remove themselves from a search prior to its conclusion. Likewise, the negative association between repeated years on the job market and offers might reflect fundamental problems with the quality of an application, or more complex factors such as geographical constraints. As we did not collect data that would allow us to determine the quality of application, or the fit of an application to a particular opening, we cannot evaluate these metrics beyond the broad associations found in our dataset. Additionally, other unmeasured factors (e.g. applicant pedigree) are likely important considerations, consistent with recent data implicating institutional prestige and non-meritocratic factors in faculty hiring ( Clauset et al., 2015 ). This should be a major consideration for future studies of the academic job market.

When examining publication-related metrics, we found that total citation counts were significantly associated with receiving a job offer in both the Wilcoxon and logistic regression analyses. There was also a significant positive association between being first author on a CNS paper and receiving a job offer in the Wilcoxon analysis, but not in our logistic regression models. Examination of our data also revealed a gender gap in publication metrics, with males reporting more CNS papers and more papers overall, indicating that opportunities for publication are not equally available ( Arvanitis and Cho, 2018 ; Gumpertz et al., 2017 ). Second, the results of our automated variable selection procedure suggest that being an author in any position on a paper in CNS is an advantage overall (though the result is not significant); however, within the life sciences, being the first author is more of an advantage (again, not significant). Finally, papers in CNS and other journals with high impact factors have been regarded as a major benchmark for trainees in the life sciences ( van Dijk et al., 2014 ), and qualitative comments from our applicant survey conveyed a perception that the absence of a CNS paper is deemed detrimental to offer prospects. Collectively, our data suggest that while being first author on a CNS paper increases the chances of receiving an offer (particularly in life sciences), papers in CNS were neither necessary nor sufficient for securing an offer, as the majority of our respondents received offers without having a paper in CNS.

Consistently, being the author of a CNS paper was not deemed highly important by the search committee members we surveyed. These data may reflect a discordance of priorities for individual faculty members compared to their peers and the system at-large, as recently reported ( Niles et al., 2019 ). This could lead to an unspoken expectation that faculty (especially pre-tenure faculty) see themselves as passive participants in the current academic system, instead of active participants with the authority to realign priorities through search committees ( Niles et al., 2019 ). Future studies with higher numbers of faculty respondents should endeavor to further explore this phenomenon.

Despite challenges in the job market ( Larson et al., 2014 ; Andalib et al., 2018 ; Kahn and Ginther, 2017 ), our survey revealed positive outcomes that suggest progress in select areas. Nearly half of the job applicants we surveyed reported posting at least one preprint. Several of the search committee members we surveyed confirmed that while published papers carry the most weight, preprints are generally viewed favorably. Further, despite the fact that women face numerous challenges in academia, including underrepresentation at the faculty level in most STEM departments ( Arvanitis and Cho, 2018 ; Gumpertz et al., 2017 ; Ceci and Williams, 2015 ; Leaper and Starr, 2019 ), and trail men in publication-related metrics ( Figure 3B ), our data suggest very few differences in outcomes in the May 2018–May 2019 job cycle. Both genders received similar numbers of interviews and offers, and gender-based differences in publication-related metrics persisted even when considering only the 185 individuals with offers, suggesting that committees are becoming increasingly aware of gender bias in publication-related metrics and are taking them into account when evaluating applicants ( Supplementary file 40 ).

Overall, the respondents were generally highly qualified according to the metrics we measured, and yet they reported high stress and frustration with their experiences of the faculty job search. In a large number of cases, applicants were not notified of a receipt of their application, nor were they updated on its status, given a final notice of rejection, or informed that the search may have failed. This uncertainty further complicates an already stressful process that can be mitigated by improving practices for a more streamlined application process. Applicants perceived poor mentorship as a major obstacle to their applications. Further, we found that most metrics were differentially valued by candidates and committees. Collectively, these differences in expectations between applicants and hiring institutions, coupled with the opaque requirements for obtaining a faculty position, likely drive the high stress reported by both candidates and committee members alike.

Limitations of this study and measuring outcomes in the academic job market

There are several limitations of this study imposed by both the original survey design and general concerns, such as the anonymity of respondents, and the measurability of various contributing factors. For future data collection we suggest keeping surveys focused on region-specific job markets. Our pool of applicants was largely those seeking a position in North America. We believe these results can be aggregated, but the survey questions may not all be applicable to other large markets (e.g. Europe, China, India). We did not receive a sizable response from applicants looking outside of North America and in fields outside of life sciences to make useful comparisons. A similar survey circulated in each market individually with a similar number of responses would have broader impact.

We purposely did not ask for race, ethnicity, or citizenship demographics, PhD or postdoc institution, and region or institution where offers were received. We believe the addition of these metrics could potentially jeopardize the anonymity of respondents. Despite this, these factors could be significant contributors to the receipt of an academic job offer. Racial inequalities in all STEM fields at all levels exist and need to be addressed ( Whittaker et al., 2015 ), specifically with how they intersect with gender ( Gumpertz et al., 2017 ). As indicated in our open question responses ( Figure 8B ), international postdocs may be specifically challenged in obtaining faculty job offers in the United States and Europe due to immigration policies as well as how mobility is interpreted by the job market ( Cantwell, 2011 ). The reputation of a training institution is questionably measurable, but is also often listed in anecdotal advice as important. Recently it was reported that a majority of new faculty are hired from a minority of institutions providing postdoc training ( Clauset et al., 2015 ; Miuccio et al., 2017 ). It is possible that adding institutional reputation to the other traditional metrics we measured could provide a more complete picture of the current path to a faculty position.

While we measured some of the attributes widely perceived as important in faculty hiring (e.g. funding track record), others are less easily quantified (e.g. the research proposal, lab pedigree, or letters of recommendation that comments from our search committee survey revealed to be important) and data collection on these items would be highly recommended in future surveys. Addressing the quality of application materials is highly context-specific (given the field, search committee, and institutional needs) and can improve ( Grinstein and Treister, 2018 ). Other aspects which are not directly measurable and are often cited as important for applicants in the academic job market are "fit" and "networking" ( Wright and Vanderford, 2017 ). Respondents agreed that networking, conferences, collaborations, and connections were helpful in their job search ( Figure 8A ). Conference organizers are also starting to offer badges that those searching for faculty jobs can wear at events; exploring the relationship between networking metrics (such as number of conferences and networking events attended) and success on the job market could be a topic for future research. Departmental or institutional "fit" is largely determined by the search committee on an individual basis, and it is likely that we will never be able to measure fit adequately ( Saxbe, 2019 ).

All questions in our survey were optional. We chose this survey design in order to make the survey easier for respondents to complete; however, missing answers represent a source of potential bias as unanswered questions may represent answers that could be negatively perceived and/or zero in value. For example, some individuals may not have felt comfortable indicating they had zero offers, which could lead to the offer percentages we report being inflated. Such bias could also affect the imputations in our logistic regression, and for these reasons we have attempted to provide multiple transparent and qualified analyses of the data. Future surveys may benefit from all questions requiring a response. It is also possible that participation in the survey from the outset suffers from survivorship bias, in that those applicants that had a positive experience are more likely to reflect upon it and complete a survey on the process. Our survey was also likely completed by a highly-engaged group of aspiring future faculty. The Future PI Slack group itself is a space for postdoctoral researchers most interested in obtaining a faculty career to engage with and learn from one another. Thus, the survey data likely reflects a highly motivated and accomplished group and not the full pool of applicants to faculty positions each year. Wider dissemination of future surveys will hopefully be aided by the publication of these results and increased awareness of the survey among trainees in various research communities.

Finally, the data from our survey of job applicants focused on candidates and not the search committees. It is unclear how many individual searches are represented in our dataset. It is likely that as many as ~200–500 committees were represented in our aggregated job applicant data, and different committees may adopt distinct assessment criteria. Our limited search committee survey responses show that the committees represented by our sample favor a holistic assessment of candidates and that decision by universal criteria (especially based solely on career transition awards or papers in CNS) is likely not unilateral, especially across disciplines. Future studies would benefit from surveying a larger pool of search committees to see what major trends and practices dominate, whether the majority of searches adopt a comprehensive evaluation approach, or if there is heterogeneity among committees in how tenure-track hiring assessments are conducted.

The search process for faculty jobs lacks transparency and data regarding what makes a successful applicant. Here, we began to address this deficiency through a survey targeted at the applicants themselves, and including their perceptions of the application process. Of over 300 responses by job applicants, we did not receive a single positive comment about the process, despite the fact that 58% of our participants received at least one job offer. Our data suggest that baseline thresholds exist for those more likely to receive a faculty job offer, but that many different paths can lead to a job offer. This variety of paths likely reflects both the preparation done by applicants and the different evaluation criteria used by individual search committees. For these reasons, we urge applicants not to conclude that lower than average metrics in any one area are automatically disqualifying. Indeed, we believe that increasing the transparency of the application process through systematic data collection will allow a more detailed study of the many paths to obtaining a faculty offer.

Our data also show the mental strain on applicants during the hiring process. We propose a number of potential solutions with the understanding that hiring faculty is a complex process involving multiple stakeholders. We believe the application process could be improved by simplifying the process, including standardizing application materials (e.g. requirements for research statements are similar for R1 institutions) and requesting references only after candidates are shortlisted, so that the burden of application preparation time can be reduced. Constructive feedback from mentors is vital for success during the application and interview preparation stages. Additionally, if possible, communication from search committees about unsuccessful applications would be helpful. We understand that these points may increase the workload of mentors and search committees but, if put into place, could alleviate some of the stress related to the academic job application process. In addition, applicants need to work to be sure their materials are strong and well-researched as the quality of these materials and demonstrating fit for a job posting are important to faculty on search committees ( Clement et al., 2019 ). Further work into the challenges search committees face is needed to improve their experience of the application process.

It is our hope that this and future work will not only allow all stakeholders to make informed decisions, but will also enable critical examination, discussion, and reassessment of the implicit and explicit values and biases being used to select the next generation of academic faculty. Such discussions are crucial in building an academic environment that values and supports all of its members.

Materials and methods

Survey materials.

We designed a survey (the "applicant survey") to collect demographics and metrics that were commonly discussed on Future PI Slack during the 2018–2019 academic job search cycle. The survey was designed to take less than 5 min in order to maximize response rates, and respondents were not required to answer all questions.

After collecting and performing initial analyses of this survey, we designed an additional survey for search committees (the "search committee survey"). The text of both surveys used in this work is included in the Supplementary files 41 and 42 . A Google form was used to conduct both surveys.

The applicant survey was distributed on various social media platforms including the Future PI Slack group, Twitter, and Facebook, and by several postdoctoral association mailing lists including in North America, Europe and Asia. The survey was open for approximately six weeks to collect responses.

The search committee survey was distributed to specific network contacts of the various authors. Though this distribution was more targeted, a Google form link was still used to maintain anonymity. The search committee survey was open for approximately three weeks to collect responses. In both cases, respondents to the surveys were asked to self-report, and the information collected was not independently verified. The surveys can be found in Supplementary files 41 and 42 .

Data analysis

Prior to analysis, we manually filtered out five responses in which answers were not interpretable or did not appear to answer the correct questions. Microsoft Excel and RStudio were used to graph the results of both surveys shown in Figures 1 – 6 and ​ and8. 8 . Specifically, data was filtered and subdivided using the 'tidyverse' collection of R packages, and figure plots were generated using the 'ggplot2' package. Whenever statistical analyses were used, the exact tests, p-values and χ 2 values are reported in the appropriate figure or figure legend or caption, results section and Supplementary file 7 , and represent the implementations in the basic R 'stats' package.

A p-value of less than 0.05 was considered significant. Where a number of demographics are combined in the reporting throughout this study, any analysis groups with less than five respondents were combined with other similar values instead of the raw n value in an effort to protect the anonymity of participants. Briefly, statistical methods are as follows: in general, the two-tailed Wilcoxon rank sum test (with Holm correction when applicable) or Chi-squared test was used to report p-values (see Supplementary file 7 for detailed breakdown).

The qualitative survey comments were categorized by theme (keywords/context) describing each comment and the frequency of comments pertaining to a particular theme and tabulated ( Supplementary files 17 , 18 , 38 and 39 ). Word clouds were generated using the WordItOut platform ( WordItOut, 2020 ; Figures 7 and ​ and9). 9 ). The visual summary heatmap of the job applicant perception and the survey results along with the search committee survey results ( Figure 9D ) was created by counting the frequency of comments for each metric (i.e. publications, fellowships, preprints) from the respondents to the qualitative (long answer) questions ( Supplementary files 17 , 18 , 38 and 39 ). The job applicant survey quantitative results were also used to rank metrics based on significance (as determined by Wilcoxon analysis or logistic regression analysis ( Supplementary file 7 )) and were also incorporated into the heatmap ( Figure 9D ). A number of metrics were not measured/surveyed as part of our study. These missing values are shown in gray.

Logistic regression analysis was performed in R using the 'glm' function with the 'family' parameter set to 'binomial'. All variables collected in the survey were included as independent variables, except those that were considered to be outcomes (numbers of remote interviews, onsite interviews and offers). The outcome variable was a binary 'Offer' or 'No offer' variable. All continuous variables were z-score normalized to ensure that they were centered and scaled consistently. To reduce collinearity between variables, a forward stepwise variable selection approach was adopted by starting with the variable that was most accurate in predicting offer status when included in a logistic regression model and then iteratively adding a variable to the model to maximize accuracy at every step. Furthermore, at every step, a variable would only be added if it was not correlated (Spearman correlation coefficient ≤0.5) with a variable already included in the model from a previous step. The model with the most accurate variable-combination was used to report coefficients. When multiple independent variables were considered together, missing values accounted for nearly two-thirds of the data, and were therefore imputed by fitting a bagged tree model for each variable (as a function of all the others; 63). Both variations of the analysis (missing data excluded and missing data imputed) were reported. In addition, this entire logistic regression analysis was repeated on a subset, solely comprising of applicants from the life sciences.

In order to visualize the potential paths to an offer, a decision tree was learned automatically from the data using the C5.0 algorithm ( Kuhn and Johnson, 2013 ). All possible combinations of the following parameter settings were evaluated: ( Cyranoski et al., 2011 ) either the tree-based variant or the rule-based variant of the algorithm was run, ( Ghaffarzadegan et al., 2015 ) winnowing of irrelevant variables was set to 'TRUE' or 'FALSE', and ( Schillebeeckx et al., 2013 ) the number of boosting 'trials' was set to 1, 4, 16, 32 or 64. The parameter combination with the best accuracy in predicting offer status in a 10-fold cross-validation experiment (as implemented in the 'caret' package in R) was chosen ( Kuhn, 2008 ). Since decision trees naturally handle missing values and differences in scales, no additional imputation or data normalization was performed before training and testing. The most accurate tree was found to be the one that used the rule-based variant, had no winnowing and no boosting (trials = 1) and was plotted using the 'plot' function in the 'partykit' R package ( Hothorn and Zeileis, 2015 ) and then manually grouped in Illustrator.

Acknowledgements

The authors thank Carol Greider, Feilim Mac Gabhann, Cori Bargmann, Mark Kunitomi, Needhi Bhalla, Lucia Peixoto, Sarah Stone and Dario Taraborelli for their valuable comments on an earlier version of this manuscript. The authors are members of the Future PI Slack community and would like to thank the entire Future PI Slack community and those who support them in this work.

Biographies

Jason D Fernandes is in the Department of Biomolecular Engineering, University of California, Santa Cruz, United States and is a member of the eLife Community Ambassadors programme

Sarvenaz Sarabipour is in the Institute for Computational Medicine and the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States and is a member of the eLife Early-Career Advisory Group

Christopher T Smith is in the Office of Postdoctoral Affairs, North Carolina State University Graduate School, Raleigh, United States

Natalie M Niemi is in the Morgridge Institute for Research, Madison, United States and in the Department of Biochemistry, University of Wisconsin-Madison, Madison, United States

Nafisa M Jadavji is in the Department of Biomedical Sciences Midwestern University, Glendale, United States and is a member of the eLife Community Ambassadors programme

Ariangela J Kozik is in the Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, United States

Alex S Holehouse is in the Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, United States

Vikas Pejaver is in the Department of Biomedical Informatics and Medical Education and the eScience Institute, University of Washington, Seattle, United States

Orsolya Symmons was at the Department of Bioengineering, University of Pennsylvania, Philadelphia, United States. Current address: Max Planck Institute for Biology of Ageing, Cologne, Germany

Alexandre W Bisson Filho is in the Department of Biology and the Rosenstiel Basic Medical Science Research Center, Brandeis University, Waltham, United States

Amanda Haage is in the Department of Biomedical Sciences, University of North Dakota, Grand Forks, United States

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Helena Pérez Valle, eLife, United Kingdom.

Peter Rodgers, eLife, United Kingdom.

Funding Information

This paper was supported by the following grants:

• University of North Dakota Start-up funds to Amanda Haage.
• National Institute of General Medical Sciences F32GM125388 to Jason D Fernandes.
• National Heart, Lung, and Blood Institute T32HL007749 to Ariangela J Kozik.
• Midwestern University Start-up funds to Nafisa M Jadavji.
• Washington Research Foundation Fund for Innovation in Data-Intensive Discovery to Vikas Pejaver.
• University of Washington Moore-Sloan Data Science Environments Project to Vikas Pejaver.
• Washington University in St. Louis Start-up funds to Alex S Holehouse.

Additional information

No competing interests declared.

Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing.

Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing.

Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing.

Conceptualization, Investigation, Methodology, Writing - original draft, Writing - review and editing.

Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing.

Conceptualization, Resources, Data curation, Investigation, Methodology, Project administration.

Conceptualization, Data curation, Software, Formal analysis, Validation, Visualization, Writing - review and editing.

Conceptualization, Data curation, Writing - review and editing.

Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology.

Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing.

Human subjects: This survey was created by researchers listed as authors on this publication, affiliated with universities in the United States in an effort to promote increased transparency on challenges early career researchers face during the academic job search process. The authors respect the confidentiality and anonymity of all respondents. No identifiable private information has been collected by the surveys presented in this publication. Participation in both surveys has been voluntary and the respondents could choose to stop responding to the surveys at any time. Both 'Job Applicant' and 'Search Committee' survey has been verified by the University of North Dakota Institutional Review Board (IRB) as Exempt according to 45CFR46.101(b)(2): Anonymous Surveys No Risk on 08/29/2019. IRB project number: IRB-201908-045. Please contact Dr. Amanda Haage ([email protected]) for further inquiries.

Additional files

Supplementary file 1..

Resources for finding academic jobs, often mentioned by our applicant survey respondents and cited by others as helpful for locating academic job announcements across different fields.

Supplementary file 2.

Overview of job application survey respondents’ (total and by gender) field of study. Fields which had fewer than three respondents in our job applicant survey were aggregated as “Other Fields” in the table. All percentages are calculated out of the total number of respondents.

Supplementary file 3.

Overview of candidates’ country of research origin. Regions which had fewer than five respondents in our job applicant survey were aggregated as “Other countries” in the table. All percentages are calculated out of the total number of respondents to this particular survey question (297) not the total number of overall survey respondents (n = 317).

Supplementary file 4.

Overview of the countries to which the faculty candidates applied to, for faculty positions. Note: most candidates applied to more than one country. Regions which had fewer than five respondents in our job applicant survey were aggregated as “Other countries and regions” in the table. All percentages are calculated out of the total number of respondents to this particular survey question (n = 317).

Supplementary file 5.

Overview of current academic position of our job applicant survey respondents. All percentages are calculated out of the total number of respondents to this particular survey question (n = 317).

Supplementary file 6.

Overview of time spent in postdoctoral training by our job applicant survey respondents.

Supplementary file 7.

Summary of statistical analysis. In this table and relevant figures, “ns” stands for not significant.

Supplementary file 8.

Overview of number of postdoctoral positions that the candidates held at the time of their faculty job application. All percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 9.

Overview of the job applicant publication metrics (average citation number, average h-index, average number of peer-reviewed papers, average number of preprints, average number of peer-reviewed first-author papers, number of Cell/Nature/Science journal publications or “CNS” papers of any type meaning first author, co-author or corresponding author) of our survey respondents by gender breakdown.

Supplementary file 10.

Overview of the job applicant publication metrics (average citation number, average h-index, average number of peer-reviewed papers, average number of preprints, average number of peer-reviewed first-author papers, number of Cell/Nature/Science journal publications or “CNS” papers of any type meaning first author, co-author or corresponding author) of our survey respondents in life/biomedical sciences (respondents who indicated their field of research as Chemistry, Biology, Bioengineering or Biomedical or Life Sciences) by gender breakdown.

Supplementary file 11.

Overview of the number of Cell/Nature/Science (“CNS”) journal publications of our job applicant survey respondents by gender breakdown. Percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 12.

Overview of the number of Cell/Nature/Science (“CNS”) journal publications of our job applicant survey respondents in life/biomedical sciences (respondents who indicated their field of research as Chemistry, Biology, Bioengineering or Biomedical or Life Sciences) by gender breakdown. Percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 13.

Overview of the types of funding held by our job applicant survey respondents. Percentages are calculated out of the total number of respondents to this particular survey question. All percentages are calculated out of the total number of respondents to this particular survey question. Our survey questions did not distinguish between the types (e.g. government funded vs privately funded, full vs partial salary support) or number of fellowships applied to; many of these factors are likely critical in better understanding gender differences in fellowship support.

Supplementary file 14.

Overview of candidates who had unpublished preprints at the time of their job application. Percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 15.

Overview of the responses to a twitter poll with the question: “Faculty, when you accepted your first position, how many offers did you have to choose from?”

Supplementary file 16.

Overview of candidates who also applied for non-faculty jobs (e.g. Industry positions, government jobs, etc.). Percentages are calculated out of the total number of respondents to this particular survey question (n = 315 applicants).

Supplementary file 17.

Candidate responses to “Was any aspect of your career particularly helpful when applying (preprints, grants etc.)?” Survey participants were able to provide long answers to this comment question. A word cloud referring to this table of comments is provided in Figure 8A .

Supplementary file 18.

Candidate responses to “Was any aspect of your career particularly an obstacle when applying?” Survey participants were able to provide long answers to this comment question. A word cloud referring to this table of comments is provided in Figure 8B .

Supplementary file 19.

Overview of application statistics: total number of applications made, offsite (remote via phone or online via Skype) interviews, onsite interviews, offers made, approximate number of rejections and total number of no feedbacks received from faculty job committees to our survey respondents.

Supplementary file 20.

Overview of the types of transition/independent type funding held by our faculty candidate (applicant survey) respondents. Percentages are calculated out of the total number of respondents to this particular survey question. Being a ‘Co-PI’ of a grant as a postdoctoral researcher or research scientist means co-writing a grant with a PI (an independent investigator). The co-writer may or may not be explicitly mentioned on the grant as a Co-PI.

Supplementary file 21.

Overview of Candidates who had approved or pending patents from their research at the time of their job application. Percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 22.

Overview of candidates who were familiar with the Future PI Slack resource and other resources during their application process. Responses to “Did you find the Future PI google sheet/Slack helpful? Yes/No” Survey participants were able to provide a long answer to this comment question (Future PI Slack or FPI Slack is a Slack group comprised of postdoctoral researchers aspiring to apply for faculty/Principal Investigator positions).

Supplementary file 23.

Overview of candidates who were familiar with the Future PI Slack resource and other resources during their application process. Responses to “Why did you find the Future PI google sheet/Slack helpful?” Survey participants were able to provide a long answer to this comment question. Note: Future PI Slack is a Slack group of postdoctoral researchers who aspire to apply for faculty positions.

Supplementary file 24.

Regression analysis with stepwise variable selection was performed on the job applicant survey data. All variables collected except for the number of remote and on-site interviews were included as potential predictors of receiving ( Cyranoski et al., 2011 ) or not receiving (0) a job offer. Positive coefficients indicate positive associations and negative coefficients indicate negative associations with receiving an offer. Coefficients that are zero indicate no association. Bold values indicate that the associations were found to be significant at a threshold of 0.05. Summary of results testing criteria with offer outcomes either through Wilcoxon analyses or logistic regression. When applicants with missing values were excluded, application number (β=0.5345, p=1.53×10 −3 ), having a postdoctoral fellowship (β=0.4013, p=6.23×10 −3 ), and number of citations (β=0.4178, p=2.01×10 −2 ) positively associated with offer status in a significant manner, while searching for other jobs (β=−0.3902, p=1.04×10 −2 ) negatively associated with offer status in a significant manner. When missing values were imputed, significant positive coefficients were observed for application number (β=0.5171, p=8.55×10 −4 ), funding (β=0.3156, p=1.72×10 −2 ), having a postdoctoral fellowship (β=0.2583, p=3.75×10 −2 ) and citations (β=0.4363, p=1.34×10 −2 ). Moreover, the search for non-academic jobs (β=−0.2944, p=1.98×10 −2 ) and the number of years on the job market (β=−0.2286, p=7.74×10 −2 ) were significantly negatively associated with offer status.

Supplementary file 25.

Regression analysis with stepwise variable selection was performed on the subset of the job applicant survey data corresponding to applicants from the life sciences. All variables collected except for the number of remote and on-site interviews were included as potential predictors of receiving ( Cyranoski et al., 2011 ) or not receiving (0) a job offer. Positive coefficients indicate positive associations and negative coefficients indicate negative associations with receiving an offer. Coefficients that are zero indicate no association. Bold values indicate that the associations were found to be significant at a threshold of 0.05. Summary of results testing criteria with offer outcomes either through Wilcoxon analyses or logistic regression. When applicants with missing values were excluded, application number (β=0.5827, p=1.07×10 −3 ) and having a postdoctoral fellowship (β=0.5738, p=1.74×10 −3 ) positively associated with offer status in a significant manner, while searching for other jobs (β=−0.3975, p=3.16×10 −2 ) negatively associated with offer status in a significant manner. When missing values were imputed, significant positive coefficients were observed for application number (β=0.5445, p=4.54×10 −4 ), funding (β=0.3687, p=1.27×10 −2 ), having a postdoctoral fellowship (β=0.3385, p=1.72×10 −2 ) and citations (β=0.5117, p=1.51×10 −2 ). Moreover, the search for non-academic jobs (β=−0.3022, p=3.21×10 −2 ) and the number of years on the job market (β=−0.3226, p=3.32×10 −2 ) were significantly negatively associated with offer status.

Supplementary file 26.

Overview of job application survey respondents’ (total and by gender) applications to R1 Universities (high-activity Research Universities), PUIs (Primarily Undergraduate Institutions; see 1 for definitions) or applied to both types of institutions. Percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 27.

Overview of the teaching experience (Teaching Assistantship for a course (lecture-based and/or laboratory-based) for the course instructor only versus beyond teaching assistantship which is independently designing and instructing undergraduate and/or graduate courses) of our applicant survey respondents. Percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 28.

Overview of specific types of teaching experience of our job applicant survey respondents detailed in a comment question. The “Adjunct Teaching Instructor for Undergraduate Courses at a Community College or PUI” and “Adjunct Teaching Instructor for Undergraduate Courses at an R1 or PU Institution” were explicitly mentioned in comments by our applicant survey respondents. The “Total Adjunct teaching positions” were the total head-count of “adjunct type” college teaching performed by our job applicant survey respondents. A total of n = 162 applicants responded to this comment type long answer question.

Supplementary file 29.

Overview of candidates who commented on their view in general of the application process. Responses to “Do you have any comments that you would like to share? For example, how did you experience the application process?” Survey participants were able to provide a long answer to this comment question. A word cloud referring to this table of comments is provided in Figure 8C . Percentages are calculated out of the total number of respondents to this particular survey question.

Supplementary file 30.

Overview of number of times job candidate survey respondents applied for a faculty (PI) position ( Box 1 ). This is in response to the survey question:”How many times have you applied for PI positions? i.e. if the 2018–2019 cycle was the first time, please enter "1", if you also applied last cycle, enter "2", etc. Percentages are calculated out of the total number of respondents to this particular survey question (n = 314).

Supplementary file 31.

Overview of candidates who commented on their view in general of the application process. Responses to “Do you have any comments that you would like to share? For example, how did you experience the application process?” Survey participants were able to provide long answers to this comment question. A word cloud referring to this table of comments is provided in Figure 8C .

Supplementary file 32.

Overview of search committee members who commented on “Do you have any other comments or thoughts about the state of hiring for tenure track positions?” Survey participants were able to provide a long answer to this comment question.

Supplementary file 33.

Overview of the search committee survey responses to “Approximately how many applicants for a posted position do you get?”, “Approximately how many applicants make it through the first round of cuts?”, “Approximately how many applicants are invited for off-site interview (Skype/phone)?”, “Approximately how many offers does your committee make per job posting?”, “Approximately how many openings has your department had in the last five years?”, “Approximately how many applicants are invited for on-site interview?”, “How long have you been involved in academic search committees?”.

Supplementary file 34.

Overview of the search committee faculty demographics of our faculty survey respondents. Percentages are calculated out of the total number of respondents to this particular survey question (n = 15).

Supplementary file 35.

Overview of the search committee survey responses to “Does your committee look favorably upon preprints?”.

Supplementary file 36.

Overview of the search committee survey responses to “What is your perception of the job market for tenure track faculty as someone involved in the search process (please tick all that are true)”. Percentages are calculated out of the total number of respondents to this particular survey questions ( Rockey, 2012 ).

Supplementary file 37.

Overview of the search committee survey responses to evaluation of a number of the tenure-track application materials: 1) “To what extent does the research proposal weigh on the selection process (e.g. "This candidate's research statement is incredibly compelling!", 2) “To what extent does good mentorship in the candidate's postdoctoral/graduate student lab explicitly weigh on selection process (e.g. "This candidate's mentor is known to produce good trainees", 3) “How heavily does the committee weigh graduate student fellowships or awards (e.g. The National Science Foundation (NSF) Graduate Research Fellowship (GRF), The National Institutes of Health (NIH) predoctoral fellowship/The Ruth L. Kirschstein National Research Service Awards for Individual Predoctoral Fellowships (F30 or F31), etc.)”, 4) “How heavily does the committee weigh non-transitional postdoctoral fellowships or awards (e.g. NIH F32, AHA etc.)”, 5) “Does your committee weigh Cell, Science, or Nature papers above papers in other journals?”, 6) “To what extent does journal impact factor explicitly weigh in to the selection process (e.g. does the word ‘impact factor’ come up in discussions around applicants)?”, 7)”How heavily does the committee weigh transition awards as a positive factor (i.e. The NIH Pathway to Independence (K99/R00) award, Burroughs Wellcome Career Award, or another award that provides the applicant with money as a new faculty member)?”, 8)”How heavily does the committee weigh prior teaching experience?”. In the survey, a 5-level Likert scale was used to record faculty impressions where a response of 1 = not at all and 5 = heavily. Percentages are calculated out of the total number of respondents to this particular survey question (n = 15).

Supplementary file 38.

Overview of the search committee who responded to “What information do you wish more candidates knew when they submitted their application?” Survey participants were able to provide a long answer to this comment question. A word cloud referring to this table of comments is provided in Figure 10A .

Supplementary file 39.

Overview of search committee faculty members who commented on “Have you noticed any changes in the search process since the first search you were involved in?” Survey participants were able to provide a long answer to this question. A word cloud referring to this table of comments is provided in Figure 10B .

Supplementary file 40.

Mean and median values for publication-related metrics plotted in Figure 2B broken down by gender and offer status. Additionally, p-values from Wilcoxon rank-sum tests that compare metric values from the female and male groups. “All” shows these values when the full dataset is considered, “With offers” shows values for only those applicants with at least one offer, and “Without offers” shows values for only those without any offers. “F” stands for female and “M” stands for male. Trends in gender differences remain the same even for the applicants with offers, serving as a possible explanation for the similar search outcomes for females and males and the importance of gender in the logistic regression.

Supplementary file 41.

Survey of the applicants to the tenure-track jobs.

Supplementary file 42.

Survey of faculty members involved in tenure-track searches.

Transparent reporting form

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Decision letter

Adriana bankston.

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by two members of the eLife Features Team. The following individuals involved in review of your submission have agreed to reveal their identity: Adriana Bankston (Reviewer #2).

This article (which is based on a survey of more than 300 early-career researchers who are/were on the job market) has the potential to be important. However, the data require further analysis and the presentation and discussion need to be improved.

Essential revisions:

1. The authors should remove the Twitter poll and related analysis from this paper, leaving just the main survey and the faculty survey.

2. Throughout the manuscript, the authors seek to minimize the importance of papers published in Cell, Nature or Science (CNS). However, there are at least two instances in which having a CNS paper appears to be the strongest predictor of job market success (Table S7 and Figure 4B). Additionally, prior studies have also suggested the same (see Pinheiro et al., 2014; and links to multiple peer reviewed studies in Cuff, 2017). It is absolutely critical that the instances where having a CNS paper appears to be the strongest predictor of job market success be noted and described in the text. (See comments from reviewer #1 for a fuller discussion of this issue).

- Cuff, A. J. (2017). An Academic Lottery or a Meritocracy? Inside HigerEd. Retrieved from https://www.insidehighered.com/advice/2017/05/03/phds-need-real-data-how-potential-employers-make-hiring-decisions-essay.

- Pinheiro, D., Melkers, J., & Youtie, J. (2014). Learning to play the game: Student publishing as an indicator of future scholarly success. Technological Forecasting & Social Change, 81, 56-66. doi:10.1016/j.techfore.2012.09.008

3. Men have more first author publications than women particularly in CNS journals - the authors should comment on this.

4. In Figure 4D the authors should identify the factors that differentiate between candidates with one or more offers vs. those who received none.

5. Regarding the logistic regression: rather than conducting an analysis with 16 variables, many of which are collinear, the authors should conduct this analysis one variable at a time to identify the single features that predict job market success. The authors can then test these variables for collinearity and combine uncorrelated variables into models that include two, three, four, etc. independent predictors.

6. 96% of the respondents with CNS publications were in fields in "Biomedical or life sciences" or "Biology (other)" as described in Figure 1A. The authors should therefore repeat several of the key analyses on success predictors on the life science cohort alone, including making a new version of Figure 4 that includes data for the life science cohort alone. The authors should also update the tables S7, S9, S10 and S22 to include statistics about CNS publications done for the life scientists in the cohort alone (alongside the data already presented for the full cohort).

7. The authors conducted a "PUI-only" subgroup analysis (Figure 6), but it would also be informative to conduct an analysis among candidates who only applied to research-intensive positions. The differences between the positions may be noteworthy, and by combining too many unrelated job searches into one analysis, the authors may be missing out on important relationships.

8. The authors should provide some more background on the survey and how it was distributed at the start of the results section. What checks were in place to ensure that only early-career scientists answered it? Similarly, the authors should provide more information in the results section on how the search committee survey was distributed and who answered it.

9. This study really does not represent a cross section of different types of early career researchers from different fields nor "a wide variety of fields." Nor is it really an international applicant pool since data on race/ethnicity or nationality or citizenship status was not asked. It really represents a sample of postdocs (96%) from the biomedical and biological sciences with (72%) currently working within the U.S., Canada and U.K. who are on the job market. This is an important distinction to make because prior research Cantwell (2011), Cantwell & Taylor (2015), and Sauerman & Roach (2016) and others have done research in this area and shown how international postdocs working in the U.S. have had limited success in transitioning into tenure-track faculty positions and provide reasons to suggest why. The authors should discuss this at an appropriate place in the text and consider citing some or all of the following references:

- Cantwell, B., & Taylor, B. J. (2015). Rise of the science and engineering postdoctorate and the restructuring of academic research. Journal of Higher Education, 86(5), p 667-696.

- Cantwell, B. (2011). Transnational mobility and international academic employment: gatekeeping in an academic competition arena. Minerva: A Review of Science, Learning and Policy, 49(4), p 425-445.

- Sauermann, H. & Roach, M. (2016). Why pursue the postdoc path? Science 352:663-664. Doi: 10.1126/science.aaf2061

10. Another concern is an attempt to make correlations between number of applications with both number of interviews and offers, without taking into consideration the quality of the application, nor where the applicants received their training. There have been some large quantitative studies done by others (e.g. Clauset, Arbesman, & Larremore, 2015) that found faculty hiring follows a common and steeply hierarchical structure where doctoral prestige and where an applicant did both their Ph.D. and postdoc appointment better predicts hiring, especially in R1 institutions. The authors should discuss this concern at an appropriate place in the text and consider citing the following reference:

- Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1: e1400005. doi:10.1126/sciadv.1400005

11. Under "Statement of Ethics", the authors write that an IRB exemption was obtained on 08/29/2019. However, the survey itself was conducted in April 2019. I believe that retroactive IRB approval is generally prohibited by federal regulations. Was the IRB aware that the study had already been conducted when they granted the exemption? Can the authors comment on this serious discrepancy?

12. Teaching experience was not assessed either from the applicant or the institution perspective. Applicants self-disclosed their presumed teaching experience and then when a number of them did get job offers at R1s, the statement was made that applicants fulfill the teaching requirements for any university type. That is an oversimplification of findings. The authors' data indicates that the majority of applicants applied to R1s. This should be further discussed by the authors.

13. The authors could comment on whether the number of postdoc positions attained correlated with their ability to obtain faculty positions (in other words, are 3 postdoc appointments more desirable as compared to 1 when applying for a faculty position at a prestigious institution?).

14. The authors could address how the application process might be improved so it is a more positive experience.

Author response

[We repeat the reviewers’ points here in italic, and include our replies in Roman.]

Essential revisions: 1. The authors should remove the Twitter poll and related analysis from this paper, leaving just the main survey and the faculty survey.

We have now removed the section below from the “Applicants perceive the process to be time-consuming and opaque, with minimal to no feedback” results, including the corresponding tables:

“A separate Twitter poll indicated that applicants in general, not specifically for this cycle, typically spend more than 3 hours tailoring each application (49) (Table S26 Supplementary File 1). Our pooled applicants at minimum then spent a combined 22,932 hours (7,644 applications x 3 hours preparation each), or 2.62 years, on these applications. Individually, this number amounts to 72 hours for each applicant on average, but does not take into account how long the initial creation of “base” application materials takes, which is often a much longer process. In another follow-up Twitter poll, a majority of respondents felt that time spent on preparing faculty job applications impeded their ability to push other aspects of their career forward (Table S27 Supplementary File 1) (50).”

2. Throughout the manuscript, the authors seek to minimize the importance of papers published in Cell, Nature or Science (CNS). However, there are at least two instances in which having a CNS paper appears to be the strongest predictor of job market success (Table S7 and Figure 4B). Additionally, prior studies have also suggested the same (see Pinheiro et al., 2014; and links to multiple peer reviewed studies in Cuff, 2017). It is absolutely critical that the instances where having a CNS paper appears to be the strongest predictor of job market success be noted and described in the text. (See comments from reviewer #1 for a fuller discussion of this issue). - Cuff, A. J. (2017). An Academic Lottery or a Meritocracy? Inside HigerEd. Retrieved from https://www.insidehighered.com/advice/2017/05/03/phds-need-real-data-how-potential-employers-make-hiring-decisions-essay - Pinheiro, D., Melkers, J., & Youtie, J. (2014). Learning to play the game: Student publishing as an indicator of future scholarly success. Technological Forecasting & Social Change, 81, 56-66. doi:10.1016/j.techfore.2012.09.008

We thank the reviewers for this comment, and would like to clarify our position. We agree with the reviewers that the discussion of the importance of CNS publications is a sensitive and well-known issue. The relationship between job market success and CNS publications is complex. We have read the references suggested by the reviewers, and we agree that several studies have concluded that CNS publications are generally held in high regard by search committees. Moreover, we discuss in the text that there is a high perceived importance of CNS papers to applicants. However, our data shows 2 clear findings with regards to CNS: 1) Most applicants who get offers do *not* have a CNS paper and 2) Applicants with a CNS paper appear to have a higher chance of getting an offer than those who do not have a CNS paper. We believe that the demonstration of the first point (most candidates do not have a CNS paper) is an important point for postdocs to consider but does not detract from the 2nd point (CNS papers appear to confer an advantage to an applicant). We note that an analysis of the offer association with CNS publications is shown in Figure 4D. We observed a 7-15% increase in offer rate for candidates which had published in CNS journals. We also note that despite this observation, ~60% of our survey respondents still received at least one offer. Moreover, our data (Figure 4C) suggest that those individuals with CNS authorship who also received a job offer were more highly cited than those with CNS authorship without a job offer, suggesting that assessment of the impact of an applicant’s work is a complex blend of many factors.

Our small survey of faculty search committee members, revealed an attitude towards CNS publications that was discordant with that of the applicants and the conventional expectation. While we acknowledge that we do not have extensive information on the decision making process of every single search committee, the volume of current literature around this issue speaks to the culture shift that is occurring around academic hiring. Formal discussions of CNS as a metric and its impact on equity in the professoriate have resulted in the development of guidelines such as DORA, that reflect the burgeoning culture change. While an in-depth analysis of impact is outside the scope of this work, we look forward to future work on this topic.

We note that these results are presented in Figure 2B and 2C, as well as discussed in the “applicant scholarly metrics by gender” results subsection. How these results are in line with previous findings is presented within the introductory paragraph to that results section. We additionally revisit this point in the discussion stating “Further, despite the fact that women face numerous challenges in academia, including underrepresentation at the faculty level in most STEM departments (52,53,56,57), and trail men in publication-related metrics (Figure 2B), our data suggest very few differences in outcomes in the May 2018-May 2019 female applicant pool relative to their male counterparts. Both genders received similar numbers of interviews and offers, and gender-based differences in publication-related metrics persisted even when considering only the 185 individuals with offers, suggesting that committees are becoming increasingly aware of gender bias in publication-related metrics and are taking them into account when evaluating applicants (Table S40 Supplementary File 1). We have also now added a further point in the discussion pertaining specifically to the intersection of gender and CNS publications. “First, examination of our data revealed a gender gap in publication metrics, with males reporting more CNS authorship and publications overall, indicating that opportunities for publication are not equally available (52,53). ” Our survey has captured numerous aspects of the academic job market and the factors influencing the success of applicants. We recognize that gender (and other marginalized identities) play a large role. We believe the role of gender is thoroughly discussed in our manuscript while allowing in depth analysis of other factors.

We thank the reviewers for this comment, and for the opportunity to clarify this point within our manuscript. In our study, we performed two different analyses to understand which factors differentiate candidates based on their success in obtaining offers. We performed Wilcoxon tests (presented in Figure 4B and Table S7 Supplementary File 1) comparing the offer percentage (the number of offers/the number of applications) and also performed a logistic regression comparing candidates with offers vs those without (Table S22). The data from these two analyses are summarized in Figure 4D, as noted by the reviewer, but were confusing due to the wording in both the text and the figures. We have clarified this in the text by adding further details regarding the offer status and positive correlations in the following sentence: “When missing values were imputed, significant positive coefficients were observed for having a higher h-index, higher application numbers, career transition awards and identifying as female and obtaining an offer.” Further, we have clarified this in the figure legend for Figure 4D as follows: “Summary of significant results testing criteria associated with 1+ offer (positive) or no offers (negative) with offer outcomes either through Wilcoxon analyses (Table S7 Supplementary File 1) or logistic regression (Table S24 Supplementary File 1) ordered by decreasing effect size.”

We thank the reviewer for bringing this to our attention. As suggested by the reviewer, we have undertaken a greedy stepwise variable selection procedure in which we started with the variable that was the most predictive of offer status (1+ offer vs 0 offers) and then incrementally added one variable at a time, selecting for the best pair, triplet, and so on. While doing this we also ensured that no new variable was highly correlated or anticorrelated with a previously included variable (Absolute Spearman correlation coefficient cutoff of 0.5). This resulted in the exclusion of two variables that were highly correlated with citation count: h-index and total number of publications (see correlation plot below).

Among the remaining 14 variables, the combination of seven variables resulted in the highest accuracy in cross-validation experiments: (Applying to) Other jobs, Application number, Citations, Years on job market, Postdoc fellowships, (transition to independence) Funding and CNS (co-authorship) (see Author response table 1 below).Author response table 1.

We note that while this 7-variable model is more accurate in predicting offer status than the full model presented in the earlier version, there is a possibility of bias typically arising from the subjective decisions made during a variable selection exercise. For instance, the choice of a greedy stepwise approach, the use of Spearman correlation as a measure of collinearity, the correlation coefficient thresholds, the choice of accuracy as the quantity to maximize, among others, are all likely to influence which variables are selected and how many are deemed optimal. Nonetheless, the agreement between the single-variable analyses (Wilcoxon tests) and the more rigorous logistic regression analysis gives us confidence to update the logistic regression analysis in the manuscript to this 7-variables model (see updated Figures 4 and Table S24 Supplementary File 1). We have also modified the text summarizing these results in the “Interplay between metrics” section to read as follows:

"We implemented a rigorous variable selection procedure to maximize accuracy and remove highly correlated variables. This resulted in a model that included only seven variables (Table S24 Supplementary File 1) that was tested on a subset of applicants (n=105) who provided answers across all variables. This regression model revealed that a higher number of applications, a higher citation count and obtaining a postdoctoral fellowship were significantly associated with receipt of an offer. When missing values were imputed and the full applicant pool (n=317) was considered, all previous variables remained significant, and a significant positive coefficient was also observed for having a career transition award. In both versions of the model, the search for non-academic jobs was significantly negatively associated with offer status (Figure 4D). We note that the model with imputed data was more accurate than that with missing values excluded at distinguishing between applicants with and without offers in 10-fold cross-validation experiments. However this accuracy was found to only be 69.6%, which is insufficient to construct a usable classifier of offer status. Due to the predominance of applicants from the life sciences in our dataset, we also repeated these analyses on a subset containing only these applicants. While more variables were included in the model, the general trends remained the same, with the addition of the number of years spent on the job market as a significant negative factor in receiving an offer (Table S25 Supplementary File 1; Figure 4 – Supplement 1)."

We have updated Table S7 in Supplementary File 1 and added new tables S9 and S11 equivalents for Life/Biomedical Sciences applicants (respondents who indicated their field of research as Chemistry, Biology, Bioengineering or Biomedical or Life Sciences) scholarly metrics to the Supplementary file. The new added tables are numbered S10 and S12 in Supplementary File 1. Figure 4 – Supplement 1 shows the same analyses as in Figure 4, but restricted to the life sciences cohort. Table S25 in Supplementary File 1 reports the coefficients, p-values and other related information in the same manner as in Table S24 in Supplementary File 1.

We have updated Figure 6 to include comparisons of candidates who only applied to PUIs (PUI-focused), those who only applied to R1 institutions (R1 Focused) and candidates who applied to both. Our original observations still hold (candidates applying to PUI positions are more likely to have more extensive teaching experience). However the new analysis also reveals some interesting trends (the R1 Focused subgroup is majority male in contrast to the two other groups) that we now comment on in the “Research versus Teaching-intensive institutions” section.

We have altered the text in two separate places to provide clarity on this point. The first paragraph under “Academic Job Applicant Demographics” has been changed to include statements that participants in the survey self-identified as applicants in the academic job market in 2018-2019. The first paragraph under “Search Committees Value the Future” has been changed to state that the search committee survey was sent to a limited number of faculty, from within the authors’ professional networks, who were known to serve on search committees.

9. This study really does not represent a cross section of different types of early career researchers from different fields nor "a wide variety of fields." Nor is it really an international applicant pool since data on race/ethnicity or nationality or citizenship status was not asked. It really represents a sample of postdocs (96%) from the biomedical and biological sciences with (72%) currently working within the U.S., Canada and U.K. who are on the job market. This is an important distinction to make because prior research Cantwell (2011), Cantwell & Taylor (2015), and Sauerman & Roach (2016) and others have done research in this area and shown how international postdocs working in the U.S. have had limited success in transitioning into tenure-track faculty positions and provide reasons to suggest why. The authors should discuss this at an appropriate place in the text and consider citing some or all of the following references: - Cantwell, B., & Taylor, B. J. (2015). Rise of the science and engineering postdoctorate and the restructuring of academic research. Journal of Higher Education, 86(5), p 667-696. - Cantwell, B. (2011). Transnational mobility and international academic employment: gatekeeping in an academic competition arena. Minerva: A Review of Science, Learning and Policy, 49(4), p 425-445. - Sauermann, H. & Roach, M. (2016). Why pursue the postdoc path? Science 352:663-664. Doi: 10.1126/science.aaf2061

We thank the reviewer for this comment and note that the complete demographic data of survey participants' country of origin, field of scientific research, and current position is included in Figure 1 and detailed in Tables S2, S3 and S5 in Supplementary File 1. We acknowledge that our survey did not fully capture the difficulties faced by non-citizens in obtaining a US faculty position, though these difficulties have been demonstrated in previous literature. We have added that citizenship status was a common issue thought to hinder applicants’ progress in the last line of the last paragraph of the “Applicants perceive the process to be time-consuming and opaque, with minimal to no feedback” section and highlight its prominence in Figure 7B. We have also added the following text to the discussion of the limitations of our study: “As indicated in our open question responses (Figure 7B), international postdocs may be specifically challenged in obtaining faculty job offers in the United States and Europe due to immigration policies as well as how mobility is interpreted by the job market (59).” We note that we had previously referenced Sauermann, H. & Roach, M. (2016) in our introduction, it is reference number 9.

10. Another concern is an attempt to make correlations between the number of applications with both number of interviews and offers, without taking into consideration the quality of the application, nor where the applicants received their training. There have been some large quantitative studies done by others (e.g. Clauset, Arbesman, & Larremore, 2015) that found faculty hiring follows a common and steeply hierarchical structure where doctoral prestige and where an applicant did both their Ph.D. and postdoc appointment better predicts hiring, especially in R1 institutions. The authors should discuss this concern at an appropriate place in the text and consider citing the following reference: - Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1:e1400005. doi:10.1126/sciadv.1400005

While we acknowledge that variables such as training institution and prestige of the applicants’ graduate school and postdoctoral mentors undoubtedly influence who obtains faculty job offers, studying this relationship was beyond the scope of the current study. The current study was not designed to explore these variables because of privacy concerns for our respondents. We did not inquire about respondents’ training institutions, advisors’ prestige, or other metrics associated with their “networks” which may have influenced their job search success. Future work to explore these variables are certainly needed. We have cited Clauset et al in our discussion section when we discuss the potential impact of these training/mentoring/network variables we did not assess in the current study. From “Challenges in the Academic Job Market” in the Discussion:

“Additionally, other unmeasured factors (e.g. applicant pedigree) are likely important considerations, consistent with recent data implicating institutional prestige and non-meritocratic factors in faculty hiring (51). This should be a major consideration for future studies of the academic job market.”

We would like to add that our open-ended question regarding “What was helpful for your application” (Figure 7A) indicated applicants perceived networking methods and pedigree to be valuable in helping them land faculty jobs. So, there is clearly merit to study these metrics in future work.

We initially conducted the surveys for the Future PI Slack community only. When we decided on data analysis and wider dissemination of the results, we applied for IRB exemption for use of existing data. We have now corrected our statement of ethics to reflect the exemption criteria for publication of the survey data: “The surveys used in this manuscript were designed and implemented by the authors listed above on a voluntary basis outside of their post-doctoral positions at the time. The authors respect the confidentiality and anonymity of all respondents, and no identifiable private information was collected. Participation in both surveys was voluntary and the respondents could stop responding to the surveys at any time. The use of the data collected in these surveys was determined to meet the exemption criteria for secondary use of existing data [45 CRF 46.104 (d)(4)] by the University of North Dakota Institutional Review Board (IRB) on 08/29/2019. IRB project number: IRB-201908-045. Please contact Dr. Amanda Haage ([email protected]) for further inquiries.”

We have revised the teaching section within the results section to address the reviewers’ comments about oversimplification of the findings. The statement that applicants fulfill the teaching requirement for any university type has been removed, as requested by reviewers. Below is the revised text.

“ Levels of teaching experience varied among respondents

Discussion surrounding the academic job market is often centered on applicants' publications and/or funding record, while teaching experience generally receives much less attention. Accordingly, a candidate’s expected teaching credentials and experience vary, largely depending on the type of hiring institution. We asked applicants whether they focused their applications to a specific type of institution (R1, PUI, or both; see Box 1 for definitions), allowing us to examine teaching experience across R1 and/or PUI applicants. Most respondents applied to jobs at R1 institutions (Figure 5A), which may explain the focus on research-centric qualifications. It remains unclear what the emphasis on teaching experience is for search committees at R1 institutions, however the literature suggests that there seems to be a minimal focus (47). Additionally, there might be differences in departmental or institutional requirements that are unknown to outsiders. What is commonly accepted is that many applications to an R1 institution requires a teaching philosophy statement. The majority (99%) of our survey respondents have teaching experience (Figure 5B), with roughly half of applicants’ experience limited to serving as a Teaching Assistant (TA) (Box 1), and half reporting experience beyond a TA position, such as serving as an instructor of record (Figure 5B). The degree of teaching experience did not change based on the target institution of the applicant (Figure 5C), nor did the percentage of offers received significantly differ between groups based on teaching experience (Figure 5D).”

We did not look for a correlation between the number of postdoc positions and the ability to obtain faculty positions since the length of postdoctoral positions vary widely in our dataset (i.e. 1 postdoctoral position does not necessarily infer a shorter length of training compared to training spanning across more than one postdoctoral positions) and the majority of our respondents had a single postdoctoral appointment (see Figure 1E). In our survey, we did ask respondents for the total number of years (across all appointments) of postdoc training (Figure 1D) and this further reveals field-specific differences in expectations of postdoc appointments and length. Therefore we believe that the relationship between likelihood of receiving a faculty job offer and training extent is complex and likely requires additional considerations (e.g. quality of training over a training timespan, length of graduate training, etc) and is beyond the scope of the analysis in this paper.

We note the reviewer’s desire to provide recommendations for hiring institutions and applicants going on the job market. We have added a paragraph to the conclusion of the paper to address how the application process might be improved, so that it is a more positive experience. Additionally, we plan to share our perspective and recommendations for policy changes in a companion piece currently under preparation that provides opinion on data reported here.

The following text has been added to the conclusion of the manuscript:

“ Conclusions

The faculty job search process lacks transparency and data regarding what makes a successful applicant. Here, we began to address this need via a job market survey targeted towards the applicants themselves, including their perceptions of the application process. Of over 300 responses by job applicants, we did not receive a single positive comment on the process, despite the fact that 58% of our applicants received at least one job offer. Our data suggest that baseline thresholds exist for those more likely to receive a faculty job offer, but that many different paths can lead to a job offer. This variety of paths likely reflects both the applicant's preparation as well as different evaluation criteria used by individual search committees. For these reasons, we urge applicants not to conclude that lower than average metrics in any one area are automatically disqualifying. Indeed, we believe that increasing the transparency of the application process through systematic data collection will allow a more detailed study of the many paths to obtaining a faculty offer.

Our data show that there is a mental strain on applicants during this process, and we propose a number of potential solutions with the understanding that faculty hiring is a complex process involving multiple stakeholders. We believe the application process could be improved by simplifying the process, including standardizing application materials (e.g. requirements for research statements are similar for R1 institutions) and requesting references only after candidates are shortlisted, so that the burden of application preparation time can be reduced. Constructive feedback from mentors is vital for success during the application and interview preparation stages. Additionally, if possible, communication from search committees about unsuccessful applications would be helpful. We understand that these points may increase the workload of mentors and search committees but, if put into place, could alleviate some of the stress related to the job application process. In addition, applicants need to work to be sure their materials are strong and well-researched as the quality of these materials and demonstrating fit for a job posting are important to faculty on search committees (47). More work is needed to understand the challenges search committees face in order to improve their experience of the application process.

It is our hope that this and future work will not only allow all stakeholders to make informed decisions, but will also enable critical examination, discussion, and reassessment of the implicit and explicit values and biases being used to select the next generation of academic faculty. Such discussions are crucial in building an academic environment that values and supports all of its members.”

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A fter earning her doctorate in cell biology at the University of Pennsylvania in June, Sierra Collins did what many freshly minted Ph.D.s. do — she went looking for a job at a biotech company. But what was supposed to be a straightforward search has turned into an odyssey.

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Are you looking for excellent research opportunities for your PhD studies at the forefront of cancer research? The German Cancer Research Center (DKFZ) in Heidelberg invites international students holding a Master’s degree in (molecular) biology, ...

International Fully Funded PhD Positions in the Life Sciences in Switzerland

*New application deadlines for two open calls per year: November 1 and May 1!Life Science Zurich Graduate School offers more than 100 funded PhD positions. With around 500 research groups and more than 1600 Ph.D. students, the Life Science Zurich ...

Three 2-year Postdoctoral Fellowships at the Zukunftskolleg

(Fulltime, E 13 TV-L)Reference No: 2024/114. The preferred start date is April 1st, 2025. Conditionally on the submission of an external grant, the position can be extended for an additional year. In principle, the position can be divided into two...

Researcher in mental health / clinical neuroscience

Doctoral scholarship holder signal enhancement strategies for photoelectrochemical sensors for nucleic acid-based sequences

Let’s shape the future - University of AntwerpThe University of Antwerp is a dynamic, forward-thinking, European university. We offer an innovative academic education to more than 20000 students, c...

PhD on the impact of energy-efficient and nature-inspired lighting strategies on greenhouse crop biology

At the Department of Biosystems of the University of Leuven (Belgium), the lab of Prof. Bram Van de Poel (http://bramvandepoel.wixsite.com/vandepoel-lab) conducts research on molecular plant hormon...

Paving the way to population-oriented nutrition: rational food design combined with in vitro digestion simulations

The Laboratory of Food Technology is one of the seven research groups of the Centre for Food and Microbial Technology, Department Microbial and Molecular systems, and is located at the Campus Arenb...

1-2 doctoral (PhD) student position on computational analysis of and/or development of single-cell/spatial transcriptomics/epigenomics technologies

Do you want to contribute to top quality medical research? To be a doctoral student means to devote oneself to a research project under the supervision of experienced researchers and follow an indi...

PhD student - Unravelling the role of the microRNA cargo of extracellular vesicles in neuron-glia communication in the retina

This work will be performed in the Cellular Communication and Neurodegeneration group led by Prof. Lies De Groef in the Animal Physiology and Neurobiology Division of the Biology Department. Our re...

PhD student, Correlative Nanoscale Imaging of Molecular Turnover in Neuronal Stem Cells

Reference number PAR 2024/621The University of Gothenburg tackles society’s challenges with diverse knowledge. 56 000 students and 6 600 employees make the university a large and inspiring place to work and study. Strong research and attractive st...

PhD or postdoc position on the development of biosensors for live-cell imaging

The Lab for Nanobiology at KU Leuven specializes in the development of molecular tools, instruments, and software to study complex biological questions. We offer an interdisciplinary environment en...

Developing patient-tailored targeting of anti-inflammatory pathways in HFpEF

Dr. Elizabeth Jones and Dr. Stephane Heymans are looking for a PhD candidate working in the Cardiovascular Sciences department at KU Leuven Belgium. You will be enrolled in the PhD programme of Kat...

PhD in molecular plant biology on unraveling systemic ethylene transport in Arabidopsis

At the Department of Biosystems of the University of Leuven (Belgium), the lab of Prof. Bram Van de Poel conducts research on molecular plant hormone physiology. We have an open full-time PhD posit...

PhD position on physiological wearables to detect Early Life Stress

Job descriptionA lot of stress in the first 1,000 days from pregnancy to 2 years, can be harmful for life. Early Life Stress often goes unnoticed while it affects 1 in 8 children. The HEALS (Health...

Doctoral fellow - Department of Food Technology, Safety and Health

Last application date Aug 10, 2024 00:00Department LA23 - Department of Food Technology, Safety and HealthContract Limited durationDegree You hold a Master’s degree (preferably) in bioscience engineering, biochemistry or related disciplines with d...

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PhD Student - Department of Electronics and Information Systems

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PhD Candidate: Computational Cognitive Neuroscience – Multisensory Perceptual Inference, Learning and Attention

Employment 1.0 FTEGross monthly salary € 2,770 - € 3,539Required background Research University DegreeOrganizational unit Faculty of ScienceApplication deadline 01 August 2024Are you fascinated by how the brain makes sense of the senses in a compl...

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• Career Advice

Ph.D. Oversupply: The System Is the Problem

By  Jonathan Malloy , Lisa Young and Loleen Berdahl

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Every year, in almost every discipline, newly minted Ph.D.s outnumber tenure-track job postings by a substantial margin. While that trend has gone on for decades, most Ph.D. programs continue to maintain or even increase student enrollments and remain structured as a form of academic career training. Thus, growing numbers of Ph.D. graduates are trained for, and often expect, an academic career that’s not available to them.

North American graduate schools have made some progress to better prepare students for nonacademic careers. They have led significant innovations in professional development training, and some faculty members in both the United States and Canada have joined the discussion over Ph.D. career futures. Yet for all the talk and innovation, we hear little discussion of the underlying structural forces that maintain and perpetuate this decades-long overproduction of Ph.D.s.

Some people argue that the overproduction of Ph.D.s can be blamed on clueless faculty members unaware of “how bad it is out there.” But the available data do not support that conclusion. Our research on Canadian political science (to our knowledge, the only published study of its kind) found the vast majority of faculty were aware of the tight academic job market and were open to seeing the Ph.D. as preparation for academic and nonacademic careers. Only 15 percent felt less motivated to supervise students not planning to pursue an academic career.

In other words, the problem is not out-of-touch faculty. The problem is the system itself.

Why Ph.D. Admissions Remain High

If we graduate too many Ph.D.s, the obvious response is to admit fewer doctoral students. But which university or program will move first? Several programs froze their 2021 enrollments in the wake of COVID-19, but that is a short-term solution unlikely to signal a broader trend.

Rather, we face a classic collective action problem: we might all be better off if overall doctoral enrollments decline, but each institution, department and even faculty member benefits from maintaining or increasing their own doctoral student enrollments. The reasons are numerous, but to put it most simply, the modern university system requires Ph.D. students to keep everything else going.

In addition, the rising importance of international rankings for establishing institutional reputations and attracting students prompts research universities to try to maximize how high they rate according to various component measures. Those measures often include the number of doctoral students as a proportion of all students or the number of doctoral graduates relative to faculty members. That also creates a clear incentive to grow doctoral numbers.

And doctoral students are not just beans to be counted for international rankings. The grants that fuel the research enterprise at these institutions are structured to fund trainees (graduate students and postdoctoral scholars). Without evidence of employing and training doctoral students in past grants, a faculty member is at a disadvantage in future grant competitions.

The teaching enterprise of the modern research university is similarly fueled by armies of graduate teaching assistants grading papers, conducting labs and interacting with undergraduate students. Without them, faculty members would struggle to find time for research.

In some cases, Ph.D. students represent increased institutional revenue, as well. For public universities, that may be built into government enrollment funding formulas that reward institutions for taking in more students -- with Ph.D.s typically bringing in the most per head. Departments and programs also face incentives to sustain or grow the numbers of Ph.D.s in their discipline relative to others in order to increase the status of the unit within the institution and sometimes to maintain revenue, depending on the budget model.

Beyond those imperatives, any conversations about reducing doctoral numbers run into very real concerns about diversity and inclusion. More restrictive admissions policies can further empower graduate admissions committees to restrict the composition of the discipline in the future. As Julie Posselt demonstrated in her groundbreaking Inside Graduate Admissions , this gatekeeper function favors applicants who more closely resemble the current discipline. Reducing doctoral numbers is likely to limit efforts to achieve diversity within disciplines.

Why Program Change Is Slow

What about the other solution: Adapting programs to better align Ph.D. professional training with the realities of Ph.D. career outcomes? Ph.D. programs are increasingly tinkering to add more nonacademic professional development. Graduate faculties have led the way with more full-time professional development staff, programs for nonacademic careers and innovative ideas like the public scholars’ programs that many universities have started.

But most of those changes are Band-Aid solutions rather than effective interventions -- small ideas bolted onto programs and delivered by outside specialists rather than fundamental overhauls of those programs. The number of players also presents coordination problems. Our study of department chairs found widespread support for nonacademic professional programming but also frustration about the duplications and gaps that often result from having many different people on the campus involved.

Ideally, nonacademic employers’ needs would inform program changes. But, in fairness, it’s not clear who these nonacademic employers are: while most Ph.D.s are finding jobs eventually, little evidence suggests that employers are actively seeking Ph.D.s except in certain applied fields. For most Ph.D.s, the search for nonacademic jobs will always involve fighting against the current rather than riding with it.

How to Move Forward

The challenge of the "Ph.D. jobs crisis" is deeply structural and built into the systems of modern research universities with no simple solutions or clear consensus going forward. To push past this logjam, universities must improve communication, information and incentivization.

First, institutions need to improve internal communication about and coordination of Ph.D. career programming and placement. Graduate career development is a haphazard and disjointed affair at many universities. Graduate faculties, units and individual supervisors often operate in silos, leaving it up to students to filter and manage different messages and options.

Second, universities need to collect more information about Ph.D. job outcomes outside academe and, crucially, graduates’ satisfaction with those outcomes, and then share those data with programs and students. Programs would benefit from nuanced, discipline-specific information from employers about where and when Ph.D.-level expertise is valued and how programs can adjust and adapt. Students, especially prospective applicants, need clarity about the realities of the Ph.D. job market. Ideally, Ph.D. outcome data would be standardized and collected across institutions, allowing for consistency, comparison and transparency -- rather than letting each institution construct its own methodology and spin the data to present itself in a favorable light.

That brings us to the third response: incentivization. Universities need to establish stronger rewards for everyone to invest further in the coordination and information that we’ve described. Admittedly, much of that incentivization will come at a real cost to already cash-strapped institutions -- someone has to pay. But the university sector must recognize the reputational costs of this ongoing problem. The Ph.D. jobs crisis is not going away. And as it becomes more visible and acute, it is attracting the attention of those who would seek to slash entire programs and areas of higher education.

Those of us who work at universities and in governments, funding agencies and other organizations must all recognize and acknowledge that we are the problem . We must confront the corrosive effect of the systems we have established that depend on ever-larger intakes of Ph.D.s and make tough choices about where to draw the line or how to change programs if those graduates do not find satisfying careers when they leave. The responses required are not about simply changing attitudes. They demand we change the entire way we operate.

The End of Chevron Deference

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Dei council and dei faculty committee, biology diversity community, mit biology catalyst symposium, honors and awards, employment opportunities, faculty and research, current faculty, in memoriam, areas of research, biochemistry, biophysics, and structural biology, cancer biology, cell biology, computational biology, human disease, microbiology, neurobiology, stem cell and developmental biology, core facilities, video gallery, faculty resources, undergraduate, why biology, undergraduate testimonials, major/minor requirements, general institute requirement, advanced standing exam, transfer credit, current students, subject offerings, research opportunities, biology undergraduate student association, career development, why mit biology, diversity in the graduate program, nih training grant, career outcomes, graduate testimonials, prospective students, application process, interdisciplinary and joint degree programs, living in cambridge, graduate manual: key program info, graduate teaching, career development resources, biology graduate student council, biopals program, postdoctoral, life as a postdoc, postdoc associations, postdoc testimonials, workshops for mit biology postdocs entering the academic job market, responsible conduct of research, postdoc resources, non-mit undergraduates, bernard s. and sophie g. gould mit summer research program in biology (bsg-msrp-bio), bsg-msrp-bio gould fellows, quantitative methods workshop, high school students and teachers, summer workshop for teachers, mit field trips, leah knox scholars program, additional resources, mitx biology, biogenesis podcast, biology newsletter, department calendar, ehs and facilities, graduate manual, resources for md/phd students, preliminary exam guidelines, thesis committee meetings, guidelines for graduating, mentoring students and early-career scientists, remembering stephen goldman (1962 – 2022), 2024 workshops for mit biology postdocs entering the academic job market.

Eligibility: Any postdoc affiliated with an MIT Biology Department lab.

Application deadline : July 1, 2024. If workshops fill to capacity, applicants will be accepted on a first-come basis.

Application process : Each workshop is a stand-alone unit that does not require participation in preceding workshops.

To apply to workshops 1-3, email a statement of interest to [email protected] indicating which workshops you would like to attend, your scientific area, current lab, and whether you are applying this year or not yet.

To apply to workshop 4, fill in this form .

1. Research Vision Workshop: July 16-26, 2024

This workshop is suitable for all postdocs interested in an academic career track.

Your Research Vision is a concise summary of your overarching research focus. The ability to convey core concepts in a manner intelligible to a broad audience, and the generation of visual elements that can best deliver your message, will be useful for both job applications and interviews, and greatly enhance your success in the academic job market. Based on the model developed by the Leading Edge Fellows , this workshop will allow you to hone your Research Vision through interactive feedback and exposure to other applicants’ ideas and styles.

Format : On July 16, two postdocs transitioning to faculty after successful searches will discuss how they arrived at their Research Vision, followed by interactive Q & A. That afternoon there will be a wine-and cheese mixer for participants and instructors. The following week, participants will present their draft visions (10 min 1-slide talk, followed by 20 min discussion). Participants are encouraged to attend presentations by their peers. Another mixer will be held on July 26.

Required materials : You will need to upload an “Executive Summary” Figure and prepare a 10-minute talk using only that Figure (1 slide) by July 21.

The talk and single Figure should provide a big-picture summary that answers the following questions:

• What is the knowledge gap I hope to fill or problem I hope to solve?
• Why is it important to the field that this problem be solved?
• Why am I the best person to answer this question?
• How is my work distinct from that of my advisor or others in the field?
• Which model systems and technologies will I use or adapt in my program?

Instructors : Alicia Darnell, Alireza Ghanbarpour, Daniel Lew, Alison Ringel, Hernandez Moura Silva, and Yadira Soto-Feliciano.

2. Research Statement Writing Workshop: July 26 – August 9, 2024

This workshop is suitable for postdocs thinking of applying for an academic job in 2024 or 2025.

The Research Statement is a critical part of your application for many academic faculty jobs. A strong Research Statement introduces your track record and presents a readable, compelling, and feasible plan for your future independent research program. This workshop will allow you to hone your Research Statement using feedback from peers and experienced faculty.

Format : Participants will be given example Research Statements and advice on preparation in advance. 3-page draft statements are due on July 26. That afternoon there will be a wine-and cheese mixer for participants and instructors. Instructors will meet individually with participants on July 31 to convey feedback on first drafts. Participants submit second drafts by August 4, to discuss with instructors on August 6. Final drafts are due August 9. Another mixer will be held on August 9 at the end of the workshop.

Required materials : You will need to upload a draft statement (limit 3 pages including Figures but not references) by July 26, and you are strongly encouraged to consult your PI in preparing that draft.

Instructors : Michael Hemann, Rebecca Lamason, Daniel Lew, Peter Reddien, and Yukiko Yamashita

3. Chalk Talk Workshop: September 2024

The Chalk Talk is a common but variable component of the interview process. It focuses on your future plans, not what you have already done. In this workshop, you will first see a “mock” chalk talk on September 3 at 3 pm, and then you will have the opportunity to present your own practice chalk talk and receive feedback from faculty and peers. Please indicate whether you would like to present a practice chalk talk when you initially register. In case of excess demand, practice slots will be allocated based on participation.

Required materials : You will need to develop a chalk talk before the workshop.

Instructors : Stefani Spranger, Eliezer Calo, Iain Cheeseman, Joey Davis, Becky Lamason, Daniel Lew, Pulin Li, Sara Prescott, Alison Ringel, Francisco Sanchez-Rivera, Hernandez Moura Silva, and Brady Weissbourd.

4. Mentoring Workshop: September 17th, 2024, 1PM – 3PM

Understanding the core competencies and effective strategies of being a ment3or is an essential skill for any faculty position. This hands-on, skill-based workshop, will explore mentoring competencies and theories, strategies for effective mentorship, and tools to improve mentoring skills as a mentor. The workshop will help participants identify and develop their mentoring styles, give participants tools for successful mentoring relationships and create an action plan for mentoring in future roles.

Instructors : Hallie Dowling-Huppert and Darcy Gordon

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Tenured faculty fellow - jefferson science fellowships.

• Washington D.C.
• Reimbursement of local living expenses
• The National Academies of Science, Engineering, and Medicine

The National Academies of Sciences, Engineering, and Medicine announce a call for applications for the 2025 Jefferson Science Fellowships (JSF).

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Post-doctoral Scholar in Cancer Biology

• San Francisco, California
• compensation according to years of experience per institutional payscales
• University of California, San Francisco (UCSF)

Postdoctoral position in basic and translational cancer biology at the University of California, San Francisco.

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Assistant Professor, Neuro/Data Scientist Tenure Track

• Albuquerque, New Mexico
• $83,000-$92000
• University of New Mexico FIRST Program

UNM FIRST recruiting data & neuroscientists in neuroscience.

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Synthetic Biology Researcher – Developer (Term BioNet Fellow)

• Bedford, Massachusetts (US)
• MITRE - Moonshot

MITRE is seeking to hire several code-fluent Synthetic Biology/Biotechnology Researcher-Developers to partner with MITRE’s software engineering team.

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Evolutionary Genomics Tenure Track Faculty Position in Biology Department

• State College
• See job description.
• Pennsylvania State University

Evolutionary Genomics Tenure Track Faculty Position in Biology Department The Department of Biology in the Eberly College of Science (https://sc

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Postdoctoral Associate- Computational Spatial Biology

• Houston, TX
• Baylor College of Medicine

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Basic Sciences Faculty - Cancer Biology Program

• Richmond, Virginia (US)
• Commensurate with experience
• Massey Comprehensive Cancer Center Virginia Commonwealth University

These faculty positions are at the rank of Assistant, Associate or Full Professor and could be tenure-eligible for qualified candidates.

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Population Sciences Faculty- Cancer Prevention and Control

Is seeking highly qualified PhD, MD, or MD/PhD funded investigators with research interests in the cancer field

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Postdoctoral Research Scholar

• Iowa City, Iowa
• $61,008.00 to Commensurate + benefits
• University of Iowa Hospitals and Clinics Radiation Oncology

Postdoctoral Research Associate with Biomedical Engineering focus is available in the Department of Radiation Oncology at the University of Iowa.

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Manager, Histology Laboratory - Pathology

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Biology Laboratory Manager

• Hillsdale, Michigan
• salary commensurate with experience
• Hillsdale College

Hillsdale College is seeking an experienced laboratory technician to manage our Core Molecular Laboratory.

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Computational Research Lab Manager

• New York City, New York (US)
• $85,000.00 - $110,000.00
• The Judith Sulzberger Genome Center at the Columbia University Medical Center

Job Type: Officer of Administration Bargaining Unit: Regular/Temporary: Regular End Date if Temporary: Hours Per Week: 35.00 Standard Work S...

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Postdoctoral Associate- Proteomics and Chromatin Biology

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Principal Scientist - ADC Biology

• Washington - Bothell, Washington
• Competitive

WHY PATIENTS NEED YOU At Pfizer we deliver breakthroughs that change patients' lives. The ADC Biology team plays a vital role in this mission throu...

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Tenure Track, Assistant/Associate Professor in Plant Biochemistry/Biology

• Pullman, Washington State
• Salary range $100,000 - $135,000.Commensurate with qualifications and experience.
• Washington State University

The Institute of Biological Chemistry (IBC) at Washington State University (WSU) seeks an Assistant/Associate Professor in Plant Biochemistry/Biology

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Research Scientist 5, G-31

• Albany, New York (US)
• Commensurate with education and experience
• NYS Department of Health, Wadsworth Center

The Wadsworth Centeris seeking an outstanding scientist at the Assistant Professor level to establish a competitive, extramurally funded...

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Life Sciences Institute Sabbatical Program, University of Michigan

• Ann Arbor, Michigan
• Salary and benefits coverage are not provided during the sabbatical period.
• University of Michigan-Life Sciences Institute

The LSI is offering a 6-12 month sabbatical to use the LSI's cores with up to a $100K credit for core use.

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Assistant Professor, Neurobiology - University of Nevada, Reno

• Reno, Nevada (US)
• Assistant Professor, 9 month. See URL for more information about salary
• University of Nevada, Reno - College of Science

The Department of Biology at the University of Nevada, Reno, is looking for a tenure-track assistant professor to join us starting July 1, 2025. We...

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Visiting Assistant Professor, Ecologist/Evolutionary Biologist

• Williamstown, Massachusetts
• Williams College - Biology Department

The Biology Department at Williams College, a premier liberal arts college with a strong commitment to excellence in the sciences, invites applicat...

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Biology Faculty - Genetics

• Fort Wayne, Indiana
• The salary for this position will be discussed as part of the interview process.
• University of Saint Francis

POSITION TITLE: Biology Faculty (Genetics)  DEPARTMENT: Division of Sciences  POSITION STATUS: Full-time (9-Month)  POSITION REPORTS TO: Division D...

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Postdoctoral Associate- Cancer Biology

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Ph.d. biology.

Our PhD program accepts students possessing Bachelors or Masters degrees who are planning a career in science. The purpose of our PhD program is to prepare highly qualified scientists who have excellent up-to-date training in the fields of biology or bioinformatics, who are actively involved in scientific research, are capable of making significant contributions to their scientific field, possess all the necessary skills for effective oral and written communication with colleagues, and can successfully compete in the job market for postdoctoral and research scientist level positions in academia and science-related industries.

We currently have approximately 100 PhD students in the Biology PhD program of the School of Biological Sciences. Of recent PhD graduates, 70% are currently employed as postdoctoral researchers in academia, 9% as government scientists , 9% as industry scientists , 4% as instructors , and 9% are enrolled in further professional training . The average number of journal publications is 3.9 per student and the average number of presentations at conferences around the world is 4.3 per student .

Areas of concentration:

• Ecology, Evolution, and Behavior   including population and evolutionary ecology; community ecology; aquatic chemical ecology; ecological genomics; sensory ecology; evolution of development, behavior, and sociality; biological oceanography; environmental microbiology; theoretical ecology.
• Molecular and Cell Biology   including eukaryotic and prokaryotic cell biology; molecular physiology; molecular biophysics and structural biology; animal, plant, and microbial molecular genetics; human genomics; molecular evolution.

Quick Facts

• Stipend and tuition waiver offered.
• Fellowships available.
• Duration of program depends on research progress. Ph.D. candidates typically defend their thesis at the end of the 5th or during the 6th year.
• 18 credit hours of coursework.

General Inquiries

• Chung Kim Academic Program Coordinator Email  | 404.385.4240 | EBB 2009

Ph.D. Program Overview:

• Stipend: $33,500 per year
• Year 1 Focus:  Coursework / Lab Rotations / Teach
• Year 2 Focus:  Coursework / Thesis Research / Qualifying Exams
• Year 3 Focus:  Thesis Research
• Year 4 Focus:  Thesis Research
• Year 5 Focus:  Thesis Research, Writing and Defense

The PhD in Biology is a research-based degree involving deep immersion in a topic within biology with mentoring from an advisor and thesis committee with expertise in the field of study. Coursework is typically completed within the first 2 years (18 credit hours). Students who have previously earned an M.S. degree or taken graduate courses elsewhere can negotiate up to 9 transfer credit hours upon entering our program. Selection of a thesis advisor is made in discussion with our faculty and/or via lab rotations during the first year, and the composition of the thesis committee is established by the student by the end of the first year.

PhD students in the School of Biological Sciences are each supported by a stipend and do not pay tuition, only modest   fees   each semester. Stipend support comes from teaching or research assistantships which complement research training towards the PhD. Whether PhD stipends are earned from research or teaching assistantships after the first year is decided in consultation with a student's thesis advisor and the graduate committee. Our base PhD stipend is $33,500. Several fellowships are available which can supplement the PhD stipend.

Further Funding

Please take some time to review our   funding opportunities and fellowships .

Choosing between the Masters and Ph.D. Program

Our graduate program is primarily focused on the PhD which prepares students for careers in scientific research and employment in academia, industry, or government. We encourage potential applicants to the PhD program to directly contact individual   faculty   members whose research may appeal to them to discuss research interests and future opportunities. Students who are not yet sure of their interest in scientific research or are interested in other kinds of professional development should consider the M.S. degree in   Biology   or  Bioinformatics . Admission decisions are made by our graduate committee (composed of Biology faculty) in consultation with all of the faculty in the School of Biological Sciences.

Georgia Tech provides application fee waivers to advance diversity, recognize outstanding undergraduate achievement, and engage prospective students in recruitment events where Georgia Tech is affiliated. Such fee waivers are currently available only to domestic applicants.

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Biology related research centers.

• Aquatic Chemical Ecology Center
• Center for Biologically Inspired Design
• Center for Integrative Genomics
• Center for NanoMAD
• Center for Ribosomal Evolution and Adaptation
• Center for the Study of Systems Biology
• Integrated Cancer Research Center

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A place to discuss all things biology! We welcome people and content from all related fields.

PhD graduates and students, what is the job market like once you have your PhD?

I am about to be a senior in college studying biology, and I am looking to get my PhD in Cancer biology / cell bio / molecular bio. I was wondering what the job market is like once I graduate with my phD and if you have any advice regarding either going into academia or industry.