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Post-Doctoral Research Fellow

Job Description

Post-Doctoral Research Fellow

Full Job Description


Fred Hutchinson Cancer Center is an independent, nonprofit, unified adult cancer care and research center that is clinically integrated with UW Medicine, a world leader in clinical care, research and learning. The first National Cancer Institute-designated cancer center in the Pacific Northwest, Fred Hutch’s global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy, and COVID-19 vaccines has confirmed our reputation as one of the world’s leading cancer, infectious disease and biomedical research centers. Based in Seattle, Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy, and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover new cures to the world’s deadliest diseases and make life beyond cancer a reality.

At Fred Hutch, we believe that the innovation, collaboration, and rigor that result from diversity and inclusion are critical to our mission of eliminating cancer and related diseases. We seek employees who bring different and innovative ways of seeing the world and solving problems. Fred Hutch is in pursuit of becoming an antiracist organization. We are committed to ensuring that all candidates hired share our commitment to diversity, antiracism, and inclusion.

The Leek group is committed to being a welcoming place to all students, postdocs, faculty and collaborators (see our Code of Conduct). We work together on projects, share credit liberally, encourage and support each other, and try to solve problems that make the world a better place. We have hired economists, computer scientists, geneticists, biologists, social workers, and everything in between. We have hired people with everything from GED level education to postdoctoral level education and all are treated equally as team contributors. We support flexible work arrangements, remote work, and are committed to life-work balance in everything we do. We believe excellence is defined broadly and want to encourage people to build on their personal strengths and learn new things. We have a great track record of helping people achieve their goals in research, careers, and life and would love to work with you!

More information about how we work can be seen in our open-source guides:
  • Guide to Career Planning: https://github.com/jtleek/careerplanning
  • Guide to Data Sharing: https://github.com/jtleek/datasharing
  • Guide to Reading Papers: https://github.com/jtleek/readingpapers
  • Guide to First Paper: https://github.com/jtleek/firstpaper
  • Guide to Giving Talks: https://github.com/jtleek/talkguide
  • Guide to Writing R Packages: https://github.com/jtleek/rpackages


There is a crisis of reproducibility and replicability of scientific results. This crisis is an increasing source of concern both in the scientific and popular press. The crisis is so acute that the United States Congress is currently investigating reproducibility of the scientific process. At the heart of this crisis is a collection of problems including small-sample sizes, under-powered studies, under-trained data analysts and an inability to directly leverage prior results in the statistical analysis of smaller, hypothesis-driven experiments using high-throughput technologies.

Advances in technology have dramatically reduced the cost and difficulty of collecting high-throughput molecular data. Large collections of raw data are increasingly publicly available but are usually incorporated into individual analyses by investigators on an ad-hoc basis. Meanwhile, the other costs of running a designed, hypothesis-driven study have not decreased at the same speed with technological advances. It is still expensive to identify, recruit, collect, and follow up samples even if the high-throughput measurements themselves are cheap.
Despite the incredible amount of available public data, it is still common practice to perform statistical inference in these hypothesis-driven experiments study-by-study, only indirectly including previous data, estimates, and results. So findings from these studies may be highly variable, unreliable, or unreplicable.

This position is focused on developing statistical methods, data resources, and software and training that allow researchers to borrow strength empirically from public repositories, large-scale data generation projects, and crowd-sourced data to improve inference in individual, hypothesis driven studies. The postdoc will build on our work in developing statistical data sources, methods, software and training that facilitate and speed the work of our biological and
medical collaborators. The result will be a research community that can take advantage of public data already collected at a large cost to the NIH to improve power, reduce required sample sizes, and improve replication in many new hypothesis driven molecular studies of development and disorder.

Specifically, the postdoc will have the following duties:
  • Reading relevant literature to understand the current state of the art.
  • Developing new statistical models leveraging public and investigator collected high throughput data
  • Developing benchmarks and working together with staff to evaluate algorithms on those benchmarks.
  • Writing new R packages or Shiny Apps for distribution of statistical methodology.
  • Collaborating with computational and biomedical collaborators to evaluate and apply the developed methodologies to improve analyses.
  • Contributing to writing papers and software testing.


  • The successful applicant will have (or will be in the process of obtaining) a PhD in biostatistics, statistics, computer science, machine learning, or another statistical or computational discipline
  • Creative and thoughtful
  • Familiar with R and R packages
  • A careful writer and diligent editor
  • Excited to contribute to an important goal
  • If you are interested in this position, please submit the following materials:
  • A CV summarizing your education and work experience so far.
  • The names and email addresses of two references.
  • Two publications or preprints featuring your work.
  • A code sample representing code that you are proud of.
    • This doesn’t have to be long or especially fancy, but should be clean and do something non-trivial. Ideally this would be present as a commit to a code repository such as GitHub, but emailed code is fine as well.
Fred Hutch has a mandatory COVID-19 vaccine requirement, with exceptions only for approved medical or religious accommodations.
As a condition of employment, newly hired employees must provide proof of vaccination or initiate the accommodations process before their first day of employment.
A statement describing your commitment and contributions toward greater diversity, equity, inclusion, and antiracism in your career or that will be made through your work at Fred Hutch is requested of all finalists.

Our Commitment to Diversity: We are proud to be an Equal Employment Opportunity (EEO) and Vietnam Era Veterans Readjustment Assistance Act (VEVRAA) Employer. We are committed to cultivating a workplace in which diverse perspectives and experiences are welcomed and respected. We do not discriminate on the basis of race, color, religion, creed, ancestry, national origin, sex, age, disability (physical or mental), marital or veteran status, genetic information, sexual orientation, gender identity, political ideology, or membership in any other legally protected class. We are an Affirmative Action employer. We encourage individuals with diverse backgrounds to apply and desire priority referrals of protected veterans. If due to a disability you need assistance/and or a reasonable accommodation during the application or recruiting process, please send a request to our Employee Services Center at hrops@fredhutch.org or by calling 206-667-4700.

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