Computational postdoc positions at the Hart Lab at MD Anderson Cancer Center
Computational postdoctoral positions are available immediately in the Department of Bioinformatics and Computational Biology (DBCB) at The University of Texas MD Anderson Cancer Center in Houston, TX. CRISPR has revolutionized genetic screening in cancer cells, and the Hart Lab is defining the state of the art in the analysis of human essential genes for functional genomics and cancer targeting. We and our experimental collaborators are studying how gene essentiality varies by tissue type and genetic context, how gene mutations modulate drug efficacy, and how tumor cells adapt to survive the severe selection pressure imposed by chemotherapy. See our recent publications on screening human cell lines (Hart et al., Cell, 2015), on identifying a new therapeutic target in pancreatic cancer (http://dx.doi.org/10.1101/041996), and our Webinar describing genetic screens for cancer targeting (http://bit.ly/1XtFRP4).
We are seeking self-motivated candidates from varied backgrounds who are looking to take the next step in their careers at a world-class research institution. The MD Anderson Cancer Center is ranked No. 1 in cancer care in the United States by U.S. News and World Report, and also No. 1 in the number of awarded grants from the National Cancer Institute. It offers excellent cancer research training programs and the unmatched scientific environment of the Texas Medical Center, the world’s largest medical center that contains over 50 biomedical institutions for patient care, basic and translational research, including MD Anderson Cancer Center, Baylor College of Medicine, and Rice University. The DBCB offers an extremely competitive compensation package for computational postdocs.
The ideal candidate will have a background in systems or computational biology in model organisms, and experience with integrative analysis of large-scale and/or multidimensional data sets. Candidates with computer science or statistics degrees with some functional genomics background will also be considered. Expertise in a modern programming or analysis environment (especially Python/Pandas or R) is preferred, as is comfort working in a Unix/Linux command line environment. High throughput sequence analysis, network analysis, and a quantitative/statistical background (Bayesian inference, machine learning, etc.) are also pluses.
Interested candidates are encouraged to send a CV, a statement of research interests, and contact information for three references to Traver Hart by email at email@example.com, with the subject line “Bioinformatics/Computational Biology Postdoc Application”.