Postdoctoral Researcher — Theoretical Biophysics of Brain Organoids
Listed on 2026-08-01
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Research/Development
Research Scientist, Postdoctoral Research Fellow, Data Scientist, Physics -
Science
Research Scientist, Data Scientist, Physics
Postdoctoral Researcher — Theoretical Biophysics of Brain Organoids
The J. M. Schwarz Theory Group develops theoretical approaches to emergent phenomena in living and nonliving matter, including rigidity transitions, shape instabilities, physical learning, learning in biological systems with and without brains, and multiscale mechanics spanning chromatin, cells, tissues, and organoids. We are seeking a postdoctoral scholar to develop theoretical models describing the interplay between neuronal activity and metabolic activity in sleep–wake transitions in brain organoids.
This project is part of a broader effort to develop a quantitative physics of learning across biological scales, connecting chromatin, cells, tissues, and brain organoids through common theoretical principles.
Candidates with backgrounds in soft matter physics, statistical physics, biological physics, computational neuroscience, complex systems, machine learning for physical systems, applied mathematics, or related disciplines are encouraged to apply. The successful candidate will also have opportunities to collaborate with faculty, students, and postdoctoral researchers in the Syracuse Soft and Biological Matter Group, as well as with national and international collaborators. The position is expected to begin in September 2026 and is for one year, with the possibility of renewal for a second year.
Research duties will be to develop theoretical models describing the interplay between neuronal activity and metabolic activity in sleep–wake transitions in brain organoids and test the predictions of the modeling against experiments. Active collaboration with Professors Orly Reiner and Yaakov Nahmias and their respective group members is also important. Some mentoring of other members of the Schwarz Group will be expected.
PhDs with backgrounds in soft matter physics, statistical physics, biological physics, computational neuroscience, complex systems, machine learning for physical systems, applied mathematics, or related disciplines.
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