OverviewThe Kuznets-Speck Lab at Fred Hutch Cancer Center is seeking a highly motivated
Post Doctoral Research Fellow to develop new statistical and machine-learning methods for understanding and predicting biological function from high-dimensional 'omics data.
Our research lies at the intersection of
biostatistics, genomics, artificial intelligence, and statistical physics. We develop generative and interpretable computational frameworks for studying how cells respond to perturbations, identifying causal and predictive gene programs, reconstructing regulatory interactions, and modeling cellular state transitions. A major focus is on combining modern generative models with ideas from stochastic processes, nonequilibrium statistical physics, optimal transport, and dynamical systems to address problems in cancer biology and single-cell genomics. The Fellow will have substantial freedom to develop independent research directions within this broad program.
Mentorship and EnvironmentThe Fellow will be mentored by Assistant Professor Ben Kuznets-Speck in the Public Health Sciences Division at Fred Hutch Cancer Center. The lab will provide an interdisciplinary environment spanning statistics, machine learning, genomics, and cancer biology.
Postdoctoral Fellows will receive mentorship in research, scientific communication, grant writing, and career development, with substantial protected time for methodological research and opportunities to build collaborations with computational, experimental, and clinical groups across Fred Hutch.
This role will have the opportunity to work partially at our campus and remotely.
ResponsibilitiesPotential projects include:
- Developing generative models for single-cell and perturbation genomics, including diffusion models, flow maps, and related probabilistic methods.
- Inferring the effects of genetic, chemical, or environmental perturbations and predicting responses to unseen or combinatorial perturbations.
- Developing interpretable methods for identifying genes and regulatory programs that control cellular phenotypes, differentiation, treatment response, and disease progression, specifically in metastasis, resistance, and immune exhaustion.
- Applying these approaches to large-scale single-cell Perturb-seq, lineage-tracing, and cancer genomics datasets in collaboration with experimental and clinical investigators at Fred Hutch.
The Fellow will be encouraged to develop new methodology, produce open-source software, collaborate broadly across the center, and pursue applications that connect fundamental quantitative ideas with important questions at the heart of cancer biology.
QualificationsMINIMUM QUALIFICATIONS:- Applicants should have a PhD or equivalent doctoral degree in Statistics, Biostatistics, Computer Science, Applied Mathematics, Physics, Bioinformatics, Computational Biology, or a related quantitative field.
PREFERRED QUALIFICATIONS:- Strong foundations in statistical modeling, machine learning, applied mathematics, or statistical physics.
- Significant experience with scientific computing in Python
- Interest in developing new data-driven quantitative methodology.
- Strong communication skills and enthusiasm for collaborative interdisciplinary research.
- Experience with generative model development, and deep learning is valuable but not required.
To be considered for this role, please submit the following with your application:- A cover letter describing research interests and potential directions of interest in the lab.
- A current curriculum vitae.
- Contact information for at least three references.
The annual base salary range for this position is from $80,172 to $95,014, and pay offered will be based on experience and qualifications.
This position may be eligible for relocation assistance.
Although Fred Hutch is not sponsoring most H-1B visas at this time, candidates who already hold an H-1B sponsored by another organization and are currently in the U.S. may be eligible for this position.
Fred Hutchinson Cancer Center offers employees a comprehensive benefits package designed to enhance health, well-being, and financial security. Benefits include medical/vision, dental, flexible spending accounts, life, disability, retirement, family life support, employee assistance program, onsite health clinic, income-based child care subsidy, tuition reimbursement, paid vacation (22 days per year), paid sick leave (up to 30 calendar days per occurrence of a qualifying reason), paid holidays (13 days per year), and paid parental leave (up to 4 weeks).
Additional Information 
