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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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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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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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courses in the academic year with four (4) in the fall semester and four (4) in the spring semester (lower and upper level such as Calculus, Discrete Mathematics, Linear Algebra, and Differential Equations
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(8) undergraduate courses in the academic year with four (4) in the fall semester and four (4) in the spring semester (lower and upper level such as Calculus, Discrete Mathematics, Linear Algebra, and
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PhD in Physics Strong background in quantum mechanics and linear algebra Research experience in quantum information or quantum foundations Publication record in peer-reviewed journals Experience with
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on the charged black hole by solving the linearized Einstein equations describing perturbations of the original black hole space-time. The source term in these linearized Einstein equations is the stress-energy
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significantly influenced its academic development. The successful candidate will: -Teach undergraduate and graduate mathematics and applied mathematics courses (such as calculus, linear algebra, differential
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, employing recent analytic and numerical methodologies such as extended coordinates or pragmatic mode sum renormalization. Next, the linearized Einstein equations describing perturbations of the black hole
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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