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.
The Carter lab is seeking a talented and highly motivated post-doctoral fellow in machine-learning to analyze our unique multi-omic characterization of human cancer-tissue samples in order to elucidate basic mechanisms of cancer initiation, progression, and mestastasis. The Carter lab has pioneered the application of statistical methods to extract rich data from genomic sequencing of cancer tissue-samples and infer phylogenetic relationships between cancer subpopulations. We collaborate closely with clinical oncologists and genomic technologists across Harvard, MIT, DFCI, MGH, and the Broad Institute in order to build datasets enabling discovery of key genetic alterations driving adverse cancer outcomes.
The fellow will be expected to lead the development and implementation of novel statistical machine learning algorithms and produce usable analysis pipelines supporting our mission. The fellow will join a strong and diverse team of clinical, experimental, and quantitative researchers across our collaborating labs. Successful candidates are expected to excel at critical thinking, be quick learners for new analytical approaches, and capable of applying or developing novel computational methods for solving complex problems. The ideal candidate has both a theoretical and practical understanding of either Bayesian statistics or deep-learning techniques and has a proven track-record in areas such as statistics, mathematical modeling, complex networks data analysis, or statistical physics.
This position is suited to a person who is excited by the prospect of applying their quantitative skills to computational biology with a strong quantitative somatic genetics focus.
Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.