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, United States of America [map ] Subject Areas: Probability theory, stochastic analysis, stochastic control, interacting particle systems, large deviations, highdimensional probability and asymptotic geometric analysis Appl
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architectures and training algorithms, uncertainty quantification, high-dimensional stochastic systems and high-dimensional partial differential equation systems. Multiple positions available. About the T-5 Group
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. Mathematical techniques include qualitative and numerical analysis of deterministic and stochastic dynamical systems, optimal control, network models, and machine learning. Preference will be given to candidates
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beginning around October 2025 (although there is some flexibility in this date, in both directions ). The position is funded by NSF-EPSRC grant ‘Stochastic Shape Processes and Inference’, in collaboration
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Theory, Mechanism Design, Markov Decision Processes, probabilistic analysis, stochastic games, fair division etc. and writing and disseminating the work conducted in the context of the project. A full list
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stochastic systems plus extensive research in digital transformation and digital twins. To succeed, you must actively contribute to nurture an environment of mutual trust, dedicated collaboration and sharing
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differential equations, computational fluid dynamics, material science, dynamical systems, numerical analysis, stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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research and teaching in applied analysis, computational mathematics, data science, discrete mathematics, financial mathematics, stochastics, and statistics. The Department of Applied Mathematics is the only
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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mathematics. These positions offer excellent interdisciplinary training possibilities in scientific computing, stochastic modeling, perturbation theory, statistics, and machine learning (applied to image and