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— which is a fundamental Partial Differential Equation (PDE) that captures the time evolution of probability distributions in systems driven by stochastic processes with Brownian motion. It plays a crucial
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ecological systems with frequency-dependent selection. Planned projects use dynamical systems, stochastic differential equations and agent-based models, statistical methods for parameter inference, network and
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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fundamental mechanisms of gene regulation, and transcriptional circuits including gene-regulatory networks, feedback regulation, and regulation of stochastic expression noise in multiple model systems ranging
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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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epidemiology to understand RNA metabolism. Perform stochastic simulations to analyze model behaviors. Fit the model parameters to empirical RNA expression and RNA-protein binding data. Predict outcomes
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mathematical models to analyze and optimize waste flows. Optimization Techniques: Familiarity with advanced optimization techniques, including linear, non-linear, multi-objective, or stochastic optimization
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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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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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. 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