**Job Description**
This Postdoctoral Computational Biologist Fellow position within the Collins Genomics Lab at the Dana-Farber Cancer Institute involves leading pioneering studies on the genetic risk for cancer, leveraging large-scale genome sequencing datasets to advance clinical capabilities for early cancer detection. The role focuses on the statistical analysis and integration of large genomics datasets from cancer patients and cancer-free individuals with other data modalities (e.g., patient outcomes, functional genomics) to enable clinically relevant discoveries. This position includes collaborative work with faculty at Dana-Farber, the Broad Institute, and other institutions on cancer risk, prevention, early detection, and tumor evolution, aiming to identify novel approaches for personalized cancer prevention and improved disease interception.
**Skills & Abilities**
• Strong background in statistics and biology.
• Excellent oral and written communication skills.
• Ability to perform both self-directed and guided research.
• Outstanding personal initiative.
• 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.
• Ability to seek out mentorship and assistance as needed.
• Ability to be a mentor to other members of the community.
• Cancer biology background.
• Experience in R and/or Python (preferred).
• Experience with Linux-based shell scripting and cloud computing.
• Willingness to learn new languages and tools.
**Qualifications**
Required Degree(s) in:
• Bioinformatics
• Genetics
• Statistics
• Computer Science
• Comparably quantitative discipline (e.g., Mathematics, Physics)
**Experience**
Experience Required:
• Significant experience in computer programming and computational biological applications.
Other:
• Formal training and/or research experience in genomics (preferred).
• Experience managing and curating large datasets (desired).
Note: We’ve analyzed the actual job post using AI, for more details visit the original job post by clicking on “Apply Now”!
