Postdoctoral Research Fellow - Computational Biologist

The successful candidate will integrate large-scale germline genome sequencing with functional genomic assays (e.g., RNA-seq, chromatin profiling, single-cell multiomics) from cancer patients and controls to identify genetic mechanisms of cancer predisposition and progression. We are particularly interested in statistically rigorous approaches that connect inherited variation to gene expression, cell state-specific regulatory programs, and clinical outcomes. Related projects will include:

• Develop and apply statistical or machine learning approaches to model the effects of common and rare germline variants (including structural variants) on gene expression and cancer phenotypes in large, clinically annotated cohorts

• Integrate germline and somatic genomics with bulk and single-cell functional genomic datasets to define how inherited variation shapes cell state-specific gene regulation, somatic driver selection, and tumor evolution

• Perform population-scale genetic association and outcome analyses to identify coding and regulatory risk factors for early-onset, aggressive, and/or hereditary cancers

​The successful candidate will lead projects from conceptualization through publication, including analytical design, implementation, manuscript preparation, and presentation of findings. They will contribute to shared computational infrastructure, maintain well-documented and reproducible code, and collaborate closely with clinical and laboratory investigators. This role offers flexibility to pursue both methodological innovation and biologically driven discovery.

MINIMUM JOB QUALIFICATIONS:

• A Ph.D. in bioinformatics, computational biology, genomics, statistical genetics, or a related quantitative field, together with demonstrated expertise in large-scale genomic data analysis and significant experience in scientific programming.

• A strong background in statistics and biology. Formal training and/or research experience in genomics is preferred. Experience managing and curating large datasets is also desired.

• Excellent oral and written communication skills and the ability to perform both self-directed and guided research.

• Must demonstrate outstanding personal initiative and the ability to work effectively as part of a team.

• Ability to meet deadlines and efficiently multitask.

• Ability to effectively collaborate across a range of individuals with different comfort toward computational biology, including oral and written communication skills.

• Ability to seek out mentorship and assistance as needed, while also being a mentor to other members of our community.

KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:

• Strong background in at least one of (i) cancer biology, (ii) human genetics, and/or (iii) functional genomics, with enthusiasm for developing cross-disciplinary expertise at the intersection of these domains

• Strong proficiency in R and/or Python for statistical modeling and large-scale data analysis; experience developing reproducible computational workflows is preferred

• Experience with Linux-based environments and large-scale or cloud-based genomic analysis

• Willingness to learn new languages and tools as the field grows

SUPERVISORY RESPONSIBILITIES:
This position will involve co-mentoring of students and other researchers joining the group as described above, with opportunities for growth in developing multi-disciplinary teams that are project oriented.