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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 13 hours ago
kelly.squier@utoronto.ca ----------------------- Course Code and Title: MATA23H3 Linear Algebra I Course Description: Systems of linear equations, matrices, Gaussian elimination; basis, dimension; dot products
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 14 hours ago
kelly.squier@utoronto.ca ----------------------- Course Code and Title: MATB24H3 Linear Algebra II Course Description: Fields, vector spaces over a field, linear transformations; inner product spaces
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 13 hours ago
kelly.squier@utoronto.ca ----------------------- Course Code and Title: MATB24H3 Linear Algebra II Course Description: Fields, vector spaces over a field, linear transformations; inner product spaces
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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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at Princeton University seeks to appoint Lecturers to teach courses, sections, and/or do grading in linear algebra, calculus, or upper division math courses. Appointments may be made for one or two semesters
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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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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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calculus, linear algebra, differential equations, and basic probability at the university level Experience with scientific computing (Python, Mathematica, MATLAB, or similar) Strong written and spoken
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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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: Required: • MSc (or equivalent) in: Computer Science, Cybersecurity, Machine Learning, or related field • Strong background in: machine learning / deep learning, mathematics (probability, linear algebra