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, Teamwork, Safety, and Service Basic Qualifications: A Ph.D. degree in Mathematics, Computer Science, Physics, Chemistry, Engineering, or a related discipline. A strong foundation in linear algebra (tensor
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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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PyTorch. ✔️ You have a good knowledge of linear algebra and statistics. ✔️ You have good listening, analysis and synthesis skills, and are curious and open-minded. ✔️ You are adaptable, autonomous, rigorous
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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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with mathematical modeling and comfort with formal reasoning across algebraic, geometric, and analytic frameworks. A deep foundation in linear algebra, tensor calculus, and functional analysis; and the
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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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calculus, linear algebra, probability and statistics, and possess strong proficiency in mathematical thinking and abstract reasoning. Cellular, biochemical, molecular experimental skills Experience working
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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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clusters, cloud computing, or GPU acceleration. Strong mathematical background in linear algebra, probability, and statistics. Prior research experience with publications or preprints. The University
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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