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, CUDA, etc. Experience with numerical methods such as FDTD, FEM, BEM, etc. Basic knowledge of numerical linear algebra concepts, such as matrix factorization and decomposition algorithms. Familiarity with
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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quantum systems with exact integrability, Apply these ideas in contexts ranging from holography to resurgent quantum field theory. The project lies at the intersection of geometry, algebra, and quantum