The Broad Institute of MIT & Harvard, a world leader in cancer genome research, is looking for exceptional candidates to join the Cancer Genome Analysis group. The objective of the Cancer Genome Analysis group is to apply computational approaches to effectively collect and analyze the vast amounts of data made possible by modern technologies (such as massively parallel sequencing) and large scale collaborative projects. Successful candidates will join an interdisciplinary team of computational biologists, bioinformatics analysts, software engineers, cancer genome biologists and clinicians from the Cambridge and Boston research institutions and hospitals who are working together to identify mutations that lead to cancer, and to discover new cancer therapeutics.
We are seeking a Computational Biologist to focus on a joint effort between the Broad Institute and the Dana Farber Cancer Institute to discover and develop novel immunotherapeutic approaches to cancer treatment.
Projects will include:
- computational and statistical analysis of cancer genome and transcriptome sequencing studies,
- expansion of existing computational capabilities to more effectively identify additional types of mutations and splice variants,
- assessment of immune-related gene expression in tumor, stroma and T- and B-cell populations,
- T-cell TCR repertoire analysis and
- computational analysis of patient samples in support of ongoing clinical trials.
Critical computational contributions include careful statistical analysis of genomic alterations and their associations with each other and with clinical parameters, development and application of state-of-the-art machine learning and classification tools to carefully identify patterns in complex data, extracting robust signals from noisy experimental data, and the development of robust and stable software to facilitate future discoveries. Must thrive in an academic/professional atmosphere, where interdisciplinary teams are central to project success.
Key Responsibilities/Essential Functions:
- Methods development- analysis of RNA-seq data for splicing and gene fusion events; population and individual rearranged TCR and BCR repertoire; integrating multiple clinical and immunological data sets to identify novel discoveries and directions
- Ongoing support of a clinical effort in neoantigen discovery and application
- Self-directed computational research
- Ph.D. in mathematical, physical, computer science or engineering.
- At least two years post-PhD as post-doc or within a company is desired. Preferably with a focus on computational solutions to biological or engineering problems.
- Ability to apply statistical and machine learning techniques to solve “big data” problems.
- Significant experience in computer programming (MATLAB, R, or equivalent) with a desire to focus on computational biological applications.
- Ability to work in a highly collaborative and intellectually challenging environment.
- Excellent oral and written communication skills, to conduct both self-directed and guided computational research.
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