Postdoctoral Research Fellow - Computational Biologist

Dana-Farber Cancer Institute • Boston, MA

1d • $72,000 - $76,385

About The Position

The Collins Genomics Lab (https://labs.dana-farber.org/collins-genomics) at Dana-Farber Cancer Institute seeks a POSTDOCTORAL COMPUTATIONAL BIOLOGIST FELLOW to lead cutting-edge studies of genetic risk for cancer using large-scale multimodal sequencing datasets across tens of thousands of individuals. The main objective of these studies is to define how inherited variation influences gene regulation, tumor evolution, and clinical cancer phenotypes towards the ultimate goal of earlier cancer detection and improved prevention strategies. The ideal candidate will have expertise in cancer genomics and/or functional genomics, paired with strong computational and statistical training, and will be excited to lead independent projects at the interface of germline genetics and tumor biology. The Collins Genomics Lab offers a highly collaborative environment for this work by facilitating close collaborations with other investigators at Dana-Farber, the Broad Institute, and other institutions on cancer risk, tumorigenesis, early detection, prevention, and patient outcomes.
Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS, and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.
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.

Requirements

• 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.
• 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.
• 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

Nice To Haves

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

Responsibilities

• 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
• Lead projects from conceptualization through publication, including analytical design, implementation, manuscript preparation, and presentation of findings.
• Contribute to shared computational infrastructure
• Maintain well-documented and reproducible code
• Collaborate closely with clinical and laboratory investigators.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree