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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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applications of candidates from a wide range of backgrounds from theoretical physics to pure mathematics, with (most likely) a background in (conformal) random geometry or stochastic partial differential
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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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Deep Learning algorithms for the numerical solution of Partial or Stochastic Differential Equations (PDES or SDEs) that arise in finance and economics. Specifically, highly complex problems involving
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Tensorflow or Pytorch is advantageous Experience in numerical methods for partial differential equations is beneficial Effective communication skills and an interest in contributing to a highly international
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strong research promise in at least one of the following research domains: ◦ geometric analysis; ◦ spectral theory; ◦ partial differential equations. Previous experience in the area of the project is an
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problems in partial differential equations inspired by cutting-edge algorithms in statistics and machine learning Type of programme/project/ undertaking MAESTRO-16 Funding institution National Science Centre
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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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include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology, operator
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that uses modern differential geometry and computational mathematics for a better understanding of partial differential equations describing fluid-like phenomena in nature, such as atmospheric or oceanic