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(Partial Differential Equations: Deterministic and Probabilistic Studies), Geometry, and LIMD (Computer Logic and Discrete Mathematics). This diversity of research topics within a single laboratory reflects
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for Postdoctoral Associate positions in the broad research areas of mathematical analysis and partial differential equations (PDEs). While all applicants with a background in the analysis of PDEs will be considered
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the possibility of extension. We are looking for PhDs in Mathematics, with experience in Hyperbolic Partial Differential Equations (PDEs), or more in general, evolution equation in the sense of Petrowsky, and who
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systems, stochastic methods in statistical mechanics, hydrodynamic limits, interacting many-body systems, quantum macroscopic evolution equations, stochastic differential equations, computational methods in
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: EDPs2 (Partial Differential Equations: Deterministic and Probabilistic Studies), Geometry, and LIMD (Computer Logic and Discrete Mathematics). This diversity of research topics within a single laboratory
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, Lausanne 1015, Switzerland [map ] Subject Areas: • stochastic differential equations (SDEs); stochastic partial differential equations (SPDEs); stochastic processes on manifolds; multi-scale stochastic
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research community. The position is based in the division for Analysis, Algebra, and Dynamical Systems (LTH). The postdoctoral position is connected to the study of partial differential equations (PDEs) in
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are looking for a strong candidate to work in a team on the interdisciplinary StochMan project that integrates elements of stochastic analysis, geometry, partial differential equations, and computational
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differential equations (PDEs). While all applicants with a background in the analysis of PDEs will be considered, candidates with prior experience in theoretical physics, fluid mechanics, kinetic theory
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization