Careers

The Center for Computational Biology is seeking an experienced software engineer to be the Project Lead for HumanBase (hb.flatironinstitute.org), a dynamic and interactive web-based platform that supports thousands of biomedical researchers in exploring the molecular mechanisms of human disease. This position is ideal for an experienced engineering leader with bioinformatics experience who thrives on solving complex problems at the intersection of large-scale data, advanced analytics, and scientific discovery. This is a high-impact role that directly advances the study of human genomics and disease.

The successful candidate will lead technical direction and execution, guide other software engineers, and collaborate closely with researchers at the Center for Computational Biology to translate cutting-edge computational biology into robust software tools. The Project Lead will be a hands-on engineering leader with experience in biological data and -omics technology, as well as a complementary skillset that includes both writing high-quality code and managing a small team of software engineers. Success in this role depends on the ability to translate biological questions into computational solutions, collaborate closely with internal scientific stakeholders, and drive technically rigorous projects that advance our understanding of human biology.

The Center for Computational Biology (CCB) of the Simons Foundation’s Flatiron Institute is seeking full-time post-doctoral Flatiron Research Fellows to join its Developmental Dynamics group in 2026. The Developmental Dynamics group combines experiments, theory and computing to elucidate the contributions of encoded genomic instructions and self-organizing physical mechanisms to embryonic development. The group’s research is organized around three main themes: the mechanistic modeling of pattern formation and morphogenesis; the synthesis and decomposition of developmental trajectories; and the modeling of human developmental defects. We are looking for candidates interested in data-driven modeling of biological systems, especially in the context of behavioral changes during postembryonic development. We have devised a throughput approach for recording such changes in Drosophila and are evaluating a range of data analysis and modeling for data mining.

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 Biophysical Modeling and Genomics groups in 2026. This joint position bridges two complementary efforts within CCB. The Biophysical Modeling group focuses on the modeling and simulation of complex systems that arise in biology and soft condensed matter physics. The Genomics group develops AL/ML methods to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease. Together, these groups aim to develop integrative, hybrid models that combine deep learning for genome interpretation with mechanistic biophysical representations of its structure, dynamics, and regulation. Their joint work seeks to build theoretical and computational frameworks that link genome-scale regulatory models with quantitative, physics-based understanding of molecular and cellular organization. A key focus is on predictive modeling of how genetic variation alters the biophysical properties of neurons and the downstream phenotypic manifestations relevant to ASD and related conditions. The uniqueness of this position lies in its explicitly interdisciplinary nature—offering the opportunity to work at the intersection of AI/ML genomic modeling and mechanistic biophysical modeling—and in its close integration with directed experimental investigations within the CCBx experimental network.

The Center for Computational Biology (CCB) of the Simons Foundation’s Flatiron Institute is seeking full-time post-doctoral Flatiron Research Fellows to join its Genomics group in 2026. The Genomics group works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include developing deep learning approaches for genome interpretation; development of methods for multi-omic and spatial data analysis and integration with phenotypic and clinical data; and machine learning and AI approaches for network modeling and regulatory module detection. These and other methods are developed in tight collaboration with experimental biologists, biomedical scientists, and clinicians and are applied to specific biological problems, both fundamental and biomedical.