The Broad Institute of MIT & Harvard, a world leader in cancer genome research, is looking for an exceptional candidate to join the Sellers lab. The successful candidate will join an interdisciplinary team of bioinformatics analysts and biologists who are working together to elucidate new therapeutic hypotheses and to develop novel cancer therapeutics. We employ a range of experimental approaches including large-scale functional genomic screens, chemical biology interrogations, and the elaboration of novel tool compounds, combined with machine learning algorithms and statistical methods in pursuit of improving patient outcomes. For example, most recently we have developed new combinatorial CRISPR systems and have developed new Bayesian inference-based methods for analyzing such data. This effort has led to the discovery of synthetic lethality in NRAS-mutant cancer. The Sellers lab is expanding the project to cover a large number of patient-derived cancer cell lines across various tumor types, and to identify novel targets beyond single-gene perturbation studies. We are looking for a candidate who is enthusiastic about taking a hands-on, problem-solving approach, and collaborating with scientist in an informal collegial work environment that is infused with intellectual rigor. The right candidate will have outstanding academic records and strong communication skills, will be capable of developing innovative statistical and machine learning methods, will lead 1-2 associates, will support bioinformatic needs for the Sellers lab and manage multiple projects simultaneously, and will enjoy working in an interdisciplinary team. While a background in biology is not required it is helpful. The detailed responsibilities are listed as follows. CHARACTERISTIC DUTIES Develop data-based strategies, write new algorithms or utilize existing ones, and deploy computational tools for the analysis of large cancer and sequencing data sets. Conceive, implement and test statistical models; work with wet-lab researchers to translate these models into testable experiments; analyze the data produced from these experiments. Lead the analysis of data-rich experiments including those involving RNA-seq, CRISPR and shRNA pooled screens, gain-of-function ORF screens, proteomic data sets, and many others. Devote 80% to bioinformatics needs for the lab and mentoring associates while 20% to methodology development; support CCLE project needs and data reproducibility of various ongoing projects. Help with proposal preparation; deliver monthly results to multiple investigators; capable of handling several projects simultaneously. Publish novel findings in high impact factor journals in collaboration with wet lab members. Explore novel data visualization tools, with emphasis on integrating diverse data types. Implement algorithms as software for distribution to the global cancer research community. OTHER REQUIREMENTS A PhD degree in a quantitative discipline (such as computer science, bioinformatics, physics), or in biology with a strong quantitative background. Fast learner, analytical thinker, creative, "hands-on", highly collaborative team-player. Background in statistics and machine learning is preferred. Proficiency in R and at least one modern programming language, such as Python or C++, is required. Proficiency in bioinformatics pipelines, ranging from fastq processing to differential analysis. Strong communication skills. Knowledge of cancer genomics is a plus but is NOT required. Inclination to acquire such knowledge is. #LI-POST All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
