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, generative diffusion models, flow models, optimal transport, stochastic filtering, sequential Monte Carlo, Markov chain Monte Carlo, and Bayesian inference and inverse problems is strongly advantageous. Your
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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