Careers

Flatiron Research Fellows in CCB are individuals at the postdoctoral level with backgrounds in one or more of the following areas: applied mathematics, statistics, computational biology, biophysics, computer science, engineering, mathematical physics, or related disciplines. Reporting to Research Scientists, Data Scientists or the Center Director, as appropriate, Fellows are expected to carry out an active research program that can be independently directed and/or involve substantial collaboration with other members of CCB or the Flatiron Institute. In addition to their research, Fellows help build the rich scientific community at CCB and the Flatiron Institute by participating in seminars, colloquia, and group meetings; developing their software, mathematical and computational expertise through internal education opportunities; and sharing their knowledge through scientific publications, presentations, and/or software releases, with the financial support of the Institute. Fellows have access to the Flatiron Institute’s powerful scientific computing resources.

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 Group. The Biophysical Modeling group focuses on the modeling and simulation of complex systems that arise in biology and soft condensed matter physics. Areas of interest include the dynamics of complex and active materials, and aspects of collective behavior and self-organization in both natural systems (e.g., inside the cell) and synthetic ones. Ongoing projects focus on understanding the organization and dynamics of the nucleus, the structure and assembly of spindles, the positioning and transport of cellular organelles, and fluid-structure problems in biology. To address these, often in close collaboration with experimental collaborators, we build numerical and theoretical models from the ground up, revealing how the known mechanics of individual components give rise to collective behavior. Many such phenomena occur only within dense, highly interacting systems, inaccessible to standard techniques. To probe such regimes requires the development of fast and scalable algorithms for many-component systems, and of coarse-grained models that can be analyzed and simulated. Strong applicants with backgrounds in applied and computational mathematics, biophysics, engineering, statistical inference, and related fields are particularly encouraged to apply.