Post-Doctoral Research Fellow

Fred Hutchinson Cancer Center is an independent, nonprofit organization providing adult cancer treatment and groundbreaking research focused on cancer and infectious diseases. Based in Seattle, Fred Hutch is the only National Cancer Institute-designated cancer center in Washington.

With a track record of global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy and COVID-19 vaccines, Fred Hutch has earned a reputation as one of the world’s leading cancer, infectious disease and biomedical research centers. 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 value collaboration, compassion, determination, excellence, innovation, integrity and respect. Our mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems.

The 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 Environment

The 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.

Potential 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.

MINIMUM 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…