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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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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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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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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
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 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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computational resources scaling with the number of sources squared – which is inherently problematic numerically. Secondly, the differential equation used to evolve a magnetic system forward in time might be
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Assistant (Postdoc) to join the research team led by Univ. Prof. Olga Mula. Our group’s work sits at the forefront of numerical analysis for Partial Differential Equations, enriched with data-driven