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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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modeling, geometric deep learning or physics-informed machine learning, or you are willing to learn these quickly; strong collaboration skills: you enjoy working in a multidisciplinary team and feel
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, matrix composition, and (anisotropic) matrix architecture are influenced by the mechanical and geometric properties of their environment. These computational models can provide crucial mechanistic insights
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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