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We are seeking a creative, motivated computational biologist driven to leverage and integrate multi-omic data to reveal biological mechanisms of health and disease. The candidate will participate in the statistical design and analysis of genomic experiments (i.e. bulk RNA-seq, single-cell RNA-seq, ATAC-seq, CRISPR screens, etc.) in collaboration with wet-lab biologists. The role requires the ability to design and interpret experiments that deliver testable hypotheses that integrate clinical and biological endpoints. The candidate should have a solid foundation in applied statistics (including machine learning and graph theory), computational biology, molecular biology, a strong work ethic and the ability to work independently and in highly matrixed teams. Preference will be given to candidates with experience in identifying new mechanisms in complex cellular models. The candidate will leverage publicly available data and integrate with internally generated data. This is an exciting and interdisciplinary role that will collaborate with statistical geneticists, biomedical informaticists and wet-lab biologists. This computational biology position with the Computational Biology

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