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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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models, and associated problems in PDE, with emphasis on identifying both well- and ill-posed examples and the interplay between probabilistic analysis and the analysis of partial differential equations
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of the physical–mathematical processes governing data acquisition (described by integral operators or differential equations) with operator learning and model-aware learning techniques, in order to design
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an individual prepared to teach mathematics courses ranging from developmental through differential equations, including linear algebra and statistics. Fifteen credit hours of instruction per semester, office
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dynamics using analytical and numerical methods to solve partial differential equations, -- excellent oral and written communication skills. Prior experience in nonlinear waves, fluid dynamics and numerical
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for hyperbolic conservation laws and other time-dependent partial differential equations relevant to computational fluid dynamics. These efforts might include Bayesian physics-informed neural networks and neural
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differential equations relevant to computational fluid dynamics. These efforts might include Bayesian physics-informed neural networks and neural operators. Bayesian neural networks for approximating piecewise
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the courses attended on the following topics: Analysis of Partial Differential Equations, Continuum Mechanics and Fluid Dynamics, Functional Analysis, Numerical Analysis, Probability and Stochastic Processes
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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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using advanced mathematical tools (complex analysis, differential equations). Skills in numerical computation, particularly for material simulations. Where to apply Website https