NITMB Fellows Program
We seek early-career researchers, who will have completed a doctoral degree in Mathematics, Physics, Computer Science, Engineering, Biology or related fields by September 2025. This opportunity is open to researchers who want to develop theory and computational approaches to study any area of biology. Under the mentorship of NSF-Simons National Institute for Theory and Mathematics in Biology leadership, fellows will develop an independent research program aligned with the Institute’s interest in the constraints and capabilities of living systems, fostering collaborations with NITMB members or others outside the Institute.
This three-year fellowship offers a competitive salary and generous research stipend. The goal of the fellowship is to empower an early-career scholar to define their research path, either independently or in close collaboration with members across various departments, including Biological Sciences, Mathematics, Physics, Computer Science, and Engineering.
NITMB looks forward to welcoming the next round of NITMB Fellows in Fall 2025
Current Fellows
Maryn Carlson
Maryn Carlson is broadly interested in uncovering the genetic mechanisms by which organisms adapt to new or changing environments, at the cellular, population, and species levels. Carlson has been a postdoc in Arvind Murugan's group at the James Franck Institute, where she worked on several questions related to protein evolution. She conducted her PhD research at the University of Chicago, working with Matthias Steinruecken, on population genetic theory and inference. She previously studied plant pathology and genetics with Michael Gore at Cornell University.
Federica Ferretti
Federica Ferretti is a theoretical physicist with a background in classical equilibrium and non-equilibrium statistical mechanics, broadly interested in statistical models and quantitative approaches for biology. Ferretti obtained a PhD from La Sapienza University of Rome, with Prof. Irene Giardina as advisor. During their PhD, Ferretti worked on the development of inference methods for the collective dynamics of bird flocks and quantification of irreversibility in polar active matter. In 2022 Ferretti joined the Chakraborty group at MIT to work with Prof. Arup Chakraborty and Prof. Mehran Kardar on the adaptive immune system. Ferretti's most recent research interests include the characterization of B cell epitope immuno-dominance and stochastic aspects of affinity maturation dynamics.
Doruk Efe Gökmen
Efe Gökmen previously was a graduate student at the Institute for Theoretical Physics at ETH Zurich, following Gökmen's undergraduate studies at Bilkent University, Turkiye. As a theoretical physicist, Gökmen's expertise lies at the crossroads of machine learning, statistical physics, and information theory. Gökmen's work involves developing novel mathematical techniques and algorithms to identify collective building blocks that store the relevant information in complex systems. At NITMB, Gökmen's focus is on developing effective coarse-grained models to capture emergent hierarchical organization across multiple scales in living systems.
Alasdair Hastewell
Alasdair Hastewell’s research interests lie at the intersection of numerical applied mathematics and biophysics, combining techniques from spectral methods, optimization, and dynamical systems theory with experimental data. He works closely with experimental collaborators to develop data analysis and model inference frameworks broadly applicable across various experimental systems, from animal behavior to bacterial swarming and developmental biology. Hastewell received his Ph.D. in applied mathematics in May 2024 from the Massachusetts Institute of Technology, where his advisor was Prof. Jörn Dunkel. Before graduate school, Hastewell did his undergraduate studies at MIT in Mathematics and Physics.
Xueying Wang
Xueying Wang earned her Ph.D. in Physics from the University of Illinois, Urbana-Champaign. Wang's research tackles the dynamical properties of complex, chaotic, and out-of-equilibrium systems, including fluid turbulence, biological and artificial neural networks, ecological systems, and active matter. In her doctoral work, she developed a spatially extended stochastic ecological model of energy flow in a fluid undergoing the transition to turbulence and predicted the four different phases encountered during the progression to fully developed turbulence in the quasi-one-dimensional flow. Wang employs a combination of computational and analytical techniques derived from statistical physics in her research. She has widespread research interests ranging from fluid turbulence to generalized learning & adaptation and structural stability & emergent functionality. For more information, see personal website and Google Scholar page.
