Flatiron Research Fellow, Biological Transport Networks, CCB

The goal at CCB is to advance the understanding of fundamental and historically challenging biological processes by developing theory, innovative modeling tools for large-scale biophysical simulations, and computational frameworks for analyzing increasingly large and complex experimental datasets. Living systems are built hierarchically; as such, CCB’s research activities span several scales of biological organization, bridging the gap between microscopic detail and large-scale behaviors, and providing natural continuity between our groups’ efforts.

CCB currently comprises more than 45 research and data scientists at career stages from recent Ph.D. graduates through senior scientists, as well as visiting scientists, guest researchers, graduate students, interns, and administrative support staff. For a full description of CCB research areas and scientific staff, please see our website.

The primary objective of the Biological Transport Networks group is to unravel the intricate relationships among the architecture of transport networks, their physical and developmental constraints, and their contribution to the overall fitness of the organisms they serve. Ultimately, we seek to integrate computation, theory and experimental data to bridge our micro- and mesoscopic knowledge of living processes and the intricacies of large-scale networks.

By doing so, we aspire to unveil biological principles that can provide insights into comparative physiology across different species. Of particular interest is the modeling of transport networks across multiple scales, including their function, development and remodeling. We employ advanced computational and theoretical techniques, such as large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems.

Examples include modeling vertebrate and invertebrate circulatory systems, building transport-informed models for neuron growth and maturation, quantifying large datasets of the microvasculature, and understanding and incorporating the effects of nonlinearities into networks of vessels.

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.

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.

Responsibilities include but are not limited to:

• Performing theoretical and computational research
• Developing, implementing and maintaining scientific software
• Participating in the organization of CCB and Flatiron-wide collaborative activities including seminars, workshops and meetings
• Participating in the preparation of manuscripts for publication and of presentations at scientific conferences
• Sharing expertise and providing training and guidance to CCB staff and visitors as needed.

FRF positions are generally two-year appointments that can be renewed for a third year, contingent on performance. Fellows will be based, and have a principal office or workspace, at the Simons Foundation’s offices in New York City. Fellows may also be eligible for subsidized housing within walking distance of the Flatiron Institute.

For more information about careers at the Flatiron Institute, please  .

• Ph.D. in a relevant field(applied mathematics, statistics, computational biology, biophysics, computer science, engineering,…