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represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology
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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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computation, including but not limited to (discrete) differential geometry, statistics and computational mechanics, as well as sensor design and deployment, data analysis, mathematical modeling and strong
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represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology
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topology, computational topology, and differential geometry (e.g., Betti numbers, Hodge-Laplacian, discrete Ricci curvature) to characterize and model biopolymers such as DNA and RNA. Develop simplicial