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The Department of Mechanical Engineering at the University of Texas at Dallas is seeking highly qualified candidates for a Postdoctoral Researcher position in uncertainty quantification in experimental
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PhD project involves interdisciplinary research at the interface of computer science and mathematics and addresses a complex, coupled inverse problem with explicit uncertainty quantification. Research
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, computational fluid dynamics, and uncertainty quantification with diverse applications. Learn more: Department of Mathematics and Work at the Department of Mathematics . Furthermore, our group maintains active
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Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a
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of multi-fidelity and active learning strategies for molecular systems. The candidate will collaborate in an international research team on related research questions in machine learning, uncertainty
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
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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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or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model
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networks, risk analysis or uncertainty quantification (preferred). Knowledge of data science in general as well as practical experience with conducting data science analyses with good programming skills