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Research Council (ERC) grants. In mathematics, the most popular research areas include probability theory, analysis and partial differential equations, algebraic geometry, topology, and biomathematics. In
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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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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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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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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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solvers of high-dimensional partial differential equations, members of CHaRMNET aim to overcome this challenge. This is a one-year, benefits-eligible position that is potentially renewable on an annual
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/Qualifications - Automation or applied mathematics background, with a strong interest in physical models and numerical method - Analysis of partial differential equations, variational approach, Bayesian estimation
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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
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with new paradigms for direct solvers of high-dimensional partial differential equations, members of CHaRMNET aim to overcome this challenge. The focus of this position is on structure-preserving