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postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry
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. The areas represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry
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on online, real‑time detection, which imposes strict constraints on the computational efficiency and latency of the detector. The candidate will develop generic, broadly applicable methods that can be
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, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology, operator algebras, complex analysis and
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, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology, operator algebras, complex analysis and logic. We have
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limited to: Computational and applied mathematics; artificial intelligence for sciences (especially biomedical science, brain science, and neuroscience); data science and machine learning; modeling
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mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology, operator algebras, complex
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. The areas represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry
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computation, AI and other numerical tools for emergent quantum matter, quantum geometry, Majorana modes, fractional quantum anomalous Hall effect, and related topics. C-TEQ Fellows will be part of a vibrant