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differential problems. 2) Development of adaptive mesh generation algorithms for distributed order fractional differential equations. 3) Analysis of the stability and convergence properties of the developed
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(e.g. systems biology), or ordinary/stochastic differential equations. Experience in computational, statistical, or machine learning method development in any discipline. Experience in GPU computing
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Computational Mathematics invites applications for Postdoctoral Research Associates to join in research efforts of interest to its faculty. Domains of interest include nonlinear partial differential equations
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and classical analysis, history of mathematics, mathematical physics, partial differential equations, quantum information and quantum computing, as covered by the section Analysis and Quantum
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functional inequalities Rough paths, stochastic differential equations and stochastic PDEs Sub-Riemannian geometry The positions are full-time, fixed-term appointments, with an earliest start date on February
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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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domains. Its strengths span foundational mathematics, algebra, applied mathematics, probability and statistics, financial mathematics, and differential equations, forming a robust, interdisciplinary
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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