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government agencies (e.g. Defra, Food Standards Agency), industry bodies and F&V producers. You will develop mathematical models, based on systems of nonlinear differential equations combined with asymptotic
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functional inequalities Rough paths, stochastic differential equations and stochastic PDEs The positions are full-time, fixed-term appointments, with an earliest start date on February 1st 2026. Attractive
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are looking for: a highly motivated researcher with at least 3 years of research experience (possibly as part of the PhD) in a field related to mathematical analysis of partial differential equations (PDEs
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conjecture, -- Langlands program and related problems, -- algebraic geometry and complex geometry, -- partial differential equations and in particular Navier-Stokes equations, -- stochastic analysis
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
, environmental sciences, environmental microbiology, epidemiology, public health, or related fields. Candidate should have education and/or experience corresponding to mathematics through differential equations, a
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of Quantum Probability and Operator Algebras, Differential Geometry, Algebraic Geometry, Numerical Methods especially for Stochastic Differential Equations, Harmonic Analysis, and Classical Analysis, each
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in research, teaching & administration, which means: Collaboration and research in the field of Computational Partial Differential Equations: o Development of own solutions as part of the research
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work on optimization and optimal control related to partial differential equations with emphasis on new developments related to machine learning and data science. Your profile: • Doctorate related
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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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Zhennan Zhou's primary research interests lie in the applied analysis of differential equations and stochastic models, as well as the design and analysis of numerical methods for scientific problems