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Field
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performance computing. Topics include modelling, floating point computations, molecular dynamics, fast Fourier transforms, solving partial differential equations, parallel programming concepts, and hybrid
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Aug 31, 2027 Purpose To conduct research in analysis, the calculus of variations, geometric measure theory and partial differential equations. To contribute to the mentoring of graduate students and
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applied to regression, classification, and the solution of time-dependent partial differential equations (PDEs), such as large-scale tsunami simulations. The project involves two doctoral researchers with
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form of an ordinary or partial differential equations, and are often nonlinear. There are a few instances where analytic solutions are available and in other cases solutions can be sought through
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Applied Mathematics (Ref: FST250902) Job Description The successful candidate is expected to deliver lectures and tutorials in the areas such as real analysis, complex analysis, partial differential
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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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differential equations is a requirement. Research-level experience with development of simulation technology is a requirement. Research-level experience with either multiphase flow in porous media
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partial differential equations (PDEs). While all applicants with a background in the analysis of PDEs will be considered, candidates with prior experience in theoretical physics, fluid mechanics, kinetic
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years in following disciplines: Pure mathematics: algebraic geometry, differential geometry, partial differential equations, number theory, probability, topology. Applied mathematics: computational