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different areas of mathematics. One position will focus primarily on geometric and structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical
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structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical integration, finite-volume and finite-element methods, variational formulations
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), ideally geometric/graph neural networks, equivariant models, or generative models. Interest in applying AI to molecular or biological problems; prior structural biology experience is a plus but not required
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to ongoing work by Dr. Megha Khosla on trustworthy graph machine learning, especially on the relationship between transparency and privacy in graph-based models. Population-scale network data are highly
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applications. This includes, but is not limited to, stochastic differential equations, stochastic partial differential equations, variational and geometric methods, probabilistic numeric, optimal transport, and