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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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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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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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/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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Matrix Theory, Stochastic Differential Equations and Stochastic Processes, Partial Differential Equations and Industrial Mathematics, Topology and Dynamical Systems, Statistics and Applied Statistics
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, Stochastic Differential Equations and Stochastic Processes, Partial Differential Equations and Industrial Mathematics, Topology and Dynamical Systems, Statistics and Applied Statistics, Numerical Analysis
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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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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
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, Stochastic Differential Equations and Stochastic Processes, Partial Differential Equations and Industrial Mathematics, Topology and Dynamical Systems, Statistics and Applied Statistics, Numerical Analysis