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limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty quantification, model order reduction, data assimilation, optimal control, and their applications
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of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty quantification, model order reduction, data assimilation, optimal control
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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interactions, and will emphasize rigorous uncertainty quantification and reproducible, scalable analysis of large multi-omics datasets. The successful candidate will be expected to work closely with clinical and
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methods and depth in one or more of generative modelling, reinforcement learning, Bayesian optimisation, active learning, uncertainty quantification, causal modelling, or optimisation in complex search
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variablemodels), efficient inference methods(e.g. sampling methods), uncertainty quantification, calibration, and probabilistic forecasting, as well as the interdisciplinary application of these methods
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, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system
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that are inherently uncertain. Uncertainty quantification (UQ) seeks to characterize and propagate this uncertainty but translating it into actionable risk measures remains computationally demanding and
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importance, model selection, trait prediction, and uncertainty quantification. Substantial professional development opportunities are available, including travel and speaking opportunities at international
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discovery and inference, with an emphasis on robustness, scalability, uncertainty quantification, expert knowledge integration, and multi-scale causal abstraction and representation learning. Your Job How are