Flatiron Research Fellow - Genomics, Center for Computational Biology

About the role

The Center for Computational Biology (CCB) of the Simons Foundation’s Flatiron Institute is actively seeking enthusiastic, full-time post-doctoral Flatiron Research Fellows to join its Genomics group. CCB has a twofold mission: to develop the rigorous modeling tools and theory needed for understanding biological processes, and to create computational frameworks that will facilitate the analysis of the large, complex data sets being generated by quantitative experimental technologies. To accomplish this, CCB brings together experts in various biological domains (including genomics, molecular, cellular, and developmental biology) whose approaches are informed by the modern computational tools of applied mathematics, statistical inference, artificial intelligence/machine learning (AI/ML), biophysics, and computer science. The goal: to enable CCB to undertake ambitious, long-term research projects that transcend conventional disciplinary boundaries and that lead to a quantitative and predictive understanding of biological phenomena; and to develop high-quality, well-tested, versatile and extensible software across its research areas. CCB currently comprises more than 50 scientists at career stages from Ph.Ds through senior scientists, as well as visiting scientists and guest researchers, affiliated graduate students and interns. Together, the CCB scientific staff are pioneering an approach to biology in which theory, computation, and experimentation are integrated, from problem conceptualization onwards. This approach enables CCB to tackle not only recognized, fundamental problems in biology that have so far eluded solution, but also, because of the value placed on curiosity-driven research, to find and develop new areas of biological inquiry. Led by Olga Troyanskaya, the Genomics group focuses on genome interpretation and the molecular and regulatory networks underlying human biology and disease, with current interests in deep learning for regulatory genomics and variant interpretation; foundation models and representation learning; multi-omic, single-cell, spatial, phenotypic, and clinical data integration; and ML for network modeling and regulatory module detection. Research is carried out in