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Field
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preferences will be given to candidates: • with experience in Machine Learning and/or Partial Differential Equations and/or Numerical Analysis, • with a successful research record, • able to develop
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. Possible research topics include: Scientific machine learning Numerical partial differential equations (PDEs) Computational fluid dynamics Neural operators High-performance computing Data-driven
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Research Scholar to join in research efforts of interest to its faculty. Domains of interest include nonlinear partial differential equations, computational fluid dynamics and material science, dynamical
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methods (Kármán momentum integral method) • Numerical methods for partial differential equations • Iterative solvers for linear systems • Fluent programming in Python and/or C++ • Experience
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University of California, Los Angeles | Los Angeles, California | United States | about 2 months ago
Metabolic Flux Analysis (MFA), kinetic modeling, or constraint-based genome-scale modeling (COBRA). • Strong mathematical background in differential equations, linear algebra, and optimization. • Background
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topics such as Hamilton-Jacobi Bellman equations, stochastic optimisation frameworks, game-theoretical analysis of space logistics, high order automated differentiation, neural and Hamiltonian ODEs, data
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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Independent research and publication activities in the field of mathematical optimization with focus on nonsmooth optimization, stochastic optimization, or optimal control of partial differential equations
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must be met no later than the time the employment decision is made. At least three of: (stochastic) partial differential equations ((S)PDEs) graph theory/network science stochastic optimization and
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short-range interactions. This representation is generally known as a tensor network. Much more recently, tensor network approaches have been adapted to numerical solve partial differential equations