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interdisciplinary collaboration. Multiple postdoctoral positions are available, hosted by our diverse faculty. Research areas include but are not limited to: Inverse Problems and Imaging Science Partial Differential
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Intelligence and Machine Learning, Computational Statistics, Random Matrices, Free Probability, Stochastic Control, Mathematical Finance, Stochastic Partial Differential Equations, Markov Processes, Branching
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numerical methods for ordinary and partial differential equations, numerical analysis, mathematicalmodels in biomedicine, biostatistics, machine learning, statistical learning, multivariate statistics, time
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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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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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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization