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://www.firedrakeproject.org/Irksome/ ), a high-level, Firedrake-based software package for time-stepping finite element discretizations of partial differential equations. The postdoctoral researcher will work with the
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quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant methods, high-dimensional geometry, and structure
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modern quantitative finance. Students will master key techniques, including finite difference methods for solving partial differential equations arising in option pricing, Monte Carlo simulation and
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differential equations) with an interest in learning how to operate bioreactors in the lab. Funding category: Autre financement public Financed by the professor's research funds PHD title: PhD in Chemical
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Distribution of Effort will be 25% teaching, 70% research, and 5% departmental service. The successful candidate is expected to pursue a vigorous research program in the theory of Partial Differential
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for Partial Differential Equations with emphasis on standard and non standard finite element methods with applications to wave propagation problems and nonlinear reaction-diffusion problems. Further
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expected to teach courses such as: Calculus Linear Algebra Differential Equations Real Analysis Complex Analysis Abstract Algebra Probability Theory Numerical Methods Mathematical Modelling Applied
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, especially the areas of partial differential equations, integral equations, fast algorithms, spectral and high-order methods * machine learning, especially the areas of optimization, learning theory
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programming, Translate assignment code between Python and Matlab. Fourier analysis, differential equations, and linear algebra. Qualifications: Only Current Masters or Doctoral student registered at McGill are
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-UFSCar) Research topics include Differential Geometry and Geometric Analysis, with emphasis on: *geometric flows and evolution equations, including Ricci flow, Ricci–Bourguignon flow and mean