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"Phase-space-inspired numerical methods for high-frequency wave scattering: from semiclassical analysis through numerical analysis to implementation". The design of fast and reliable algorithms
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Functional Theory (DFT) Familiarity with artificial intelligence methods Good knowledge of electronic structure methods Experience with Linux, Git and related tools Knowledge in the field of high-performance
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in process engineering, design of experiments, numerical simulation, thin film characterizations by a combination of structural, chemical and physical methods (XRD, Raman, IRTF, SIMS, XPS, Ellipsometry
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: Applications accepted all year round Details Data assimilation combines physical models with experimental or numerical data to produce dynamically consistent flow reconstructions. In turbulence, where full
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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focused on advancing computational methods for synchrotron, free-electron laser (FEL), and related scientific challenges. SciQC combines expertise in mathematics, numerical algorithms, high-performance
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in the field of theoretical physics • the ability to perform numerical calculations Description: We are looking for one PhD student to work in the Department of Theoretical Physics at the Maria Curie
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actuarial mathematics Quantitative finance Computational and numerical methods in finance The successful candidate will play a leading role in graduate teaching, thesis supervision, and curriculum development
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Numerical relativity, machine learning Where to apply