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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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theory, integrable differential equations, determinantal point processes, Riemann-Hilbert problems, orthogonal polynomials, special functions. There are no teaching duties associated with these positions
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-driven methods to characterize demand-side flexibility using e.g., by using a set of coupled stochastics differential equations or data-driven digital twins. As a result, we will aim at obtaining a
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before the start date. You have a strong background in differential geometry and/or partial differential equations. A background in general relativity or Riemannian geometry is welcome but not mandatory
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Anemometry (LDA), and Particle Image Velocimetry (PIV). Extend existing in-house wind field models (based on stochastic differential equations such as Langevin or Fokker-Planck types). Integrate novel
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in numerical techniques used to solve ordinary and partial differential equations and be proficient with related commercial or open-source software tools (e.g., ANSYS, FEniCS, OpenFOAM or similar) and
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into the following sections: A - Algebra, Number Theory and Logic B - Analysis and Differential Equations C - Discrete Mathematics D - Geometry and Topology E - Numerical Mathematics and Scientific Computing F
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stream 7.0 overall or higher or equivalent. Have an experience with analytical and numerical solution methods for partial differential equations and working knowledge of Matlab and Latex. A full list of
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: computational harmonic analysis, approximation theory, orthogonal polynomials, image processing, inverse problems, partial differential equations, etc. It is not expected that the candidate should be an expert in
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and mathematical methods for partial differential equations and their software implementation. Proficiency with modern software development frameworks and paradigms. Knowledge in complementary emerging