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research expertise in one or more of the following areas: algebraic combinatorics, applied algebraic geometry, non-linear algebra, discrete geometry (including total positivity, cluster algebras
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in high performance scientific computing, multi-linear algebra and tensor contractions for heterogeneous exascale architectures. The successful candidate will join the NumPEx PEPR to reinforce
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foundations and principles of Machine Learning, Linear Algebra (vectorial and matricial operations, optimization), with a particular focus on Neural Networks (pytorch), 3) problem solving skills, 4) familiarity
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, tensor analysis, and network science to foster the professional development of team members. Qualifications and experience essential PhD in Applied Mathematics in the fields of Numerical Linear Algebra
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction
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linear algebra, numerical methods for PDEs and dynamical systems, stochastic methods in statistical mechanics, hydrodynamic limits, interacting many-body systems, quantum macroscopic evolution equations
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standard subjects like calculus or linear algebra, to other more advanced subjects, depending on the needs of the department and the interest and expertise of the instructor. This is a full-time (1.0 FTE), 9
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algebra, iterative solvers, preconditioning - Wave propagation problems Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7598-PIETOU-006/Default.aspx Work Location(s) Number of offers
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strong mathematical skills (e.g., probability theory, statistics, calculus, and linear algebra); Have strong communication, presentation and writing skills; Enjoy working in a multidisciplinary research
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linear algebra computations, building software for scientific applications using GPUs (Graphics Processing Unit), multi-threading and parallelism, numerical discretization methods (finite differences