Post-Doctoral Research Fellow, Single-Cell Computational Biology

Seattle, Washington State (US)
Based on experience
September 17 2020
Life Sciences
Position Type
Full Time
Organization Type

Cures Start Here. At Fred Hutchinson Cancer Research Center, home to three Nobel laureates, interdisciplinary teams of world-renowned scientists seek new and innovative ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases. Fred Hutch’s pioneering work in bone marrow transplantation led to the development of immunotherapy, which harnesses the power of the immune system to treat cancer. An independent, nonprofit research institute based in Seattle, Fred Hutch houses the nation’s first cancer prevention research program, as well as the clinical coordinating center of the Women’s Health Initiative and the international headquarters of the HIV Vaccine Trials Network. Careers Start Here.


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 Setty Lab in the Basic Sciences Division and Translation Data Science IRC at Fred Hutchinson Cancer Research Center is seeking a highly motivated post-doctoral fellow to work at the intersection of computational science, developmental & cancer biology, and single-cell genomics. The computational topics of interest include but not limited to manifold learning and dimensionality reduction, representation learning, multi-task learning, and structured learning.



The successful candidate will develop novel computational algorithms to integrate high throughput single-cell genomics data to answer fundamental questions about mechanisms driving cellular differentiation and dysregulation in cancer. They will work in collaborative partnerships with experimental biologists to design data collection experiments, model and interpret the data, and help design validation experiments.



This is a unique opportunity for someone interested in modeling and interpreting large-scale biological data generated using cutting edge high throughput technologies in a multi-disciplinary and collaborative setting. While prior experience in working with biological data is desired, we strongly encourage applications from computational scientists and machine learning researchers interested in exploring careers in computational biology.




  • Ph.D. in machine learning, data science, statistics, computational biology, or computational genomics
  • Track record of modeling and interpreting large-scale data is preferred
  • Programming skills in statistical computing (E.g.: Python, R)
  • Experience in working with biological data is desired by not necessary



Please include with your application:

  • CV
  • Summary of research interests and accomplishments
  • Contact information of three references


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 HR Operations at or by calling 206-667-4700.

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