Associate Computational Biologist, Translational Immunogenomics Lab

4 days left

Dana Farber Cancer Institute
Boston, Massachusetts, US
Apr 28, 2021
May 13, 2021
Employment Type
Full time
Salary Type
The Department of Data Science and the Translational Immunogenomics Lab (TIGL) within the Dana-Farber Center for Immuno-oncology are seeking a highly motivated and skilled computational individual who will work within an interdisciplinary team comprising computational scientists, data scientists, bioinformaticians, physicians, and biomedical scientists on developing analytical pipelines for personalized neoantigen vaccine (NeoVax) trials ongoing in the Center for Personalized Cancer Vaccines (CPCV) at Dana-Farber Cancer Institute.

We are currently seeking an Associate Computational Biologist with formal background in bioinformatics, computational biology, computer science, mathematics, physics, or statistics to support our ongoing NeoVax trials by supporting our existing analytical pipeline and developing new capabilities. The analyst will also assist in the analysis and interpretation of actual patient data as they accrue on our various trials. The position will entail interfacing with oncologists, immunologists and computational biologists within Dana-Farber and the Broad Institute.

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute brings together world renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, we provide compassionate and comprehensive care to patients of all ages; we conduct research that advances treatment; we educate tomorrow's physician/researchers; we reach out to underserved members of our community; and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.


Projects may include:

Creation and maintenance of computational pipelines using publicly available and locally developed tools for analysis of patient data
Combined analysis of data from repositories such as the The Cancer Genome Atlas project (TCGA), Genotype-Tissue EXpression (GTEx) and Cancer Cell Line Encyclopedia (CCLE) with data generated in-house for on-trial patients
Creation of pipelines for interpreting data from single-cell RNA-Seq experiments
Computational and statistical analysis of cancer genome and transcriptome sequencing studies
Packaging of tools using the Docker and Conda applications


The ideal candidate will have formal training and experience in analysis of high-throughput data using statistical or machine learning methods, and strong programming skills. Additional qualifications include:

Highly motivated and skilled individuals with a Master's or Bachelor's degree in bioinformatics, computational biology, computer science, mathematics, physics, computer science or engineering or comparable research or industrial experience.
Ability to

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