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materials and technology. Research may include analytical and numerical spectral methods, momentum- and real-space analysis, and connections between spectral structure, topology, and quantum degrees
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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
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About the Opportunity JOB SUMMARY The Blazek group performs research in cosmology, including both theoretical, observational, and numerical methods. We are involved in analysis of DES data and
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emulator of sea ice dynamics, trained using high-fidelity numerical simulations, (ii) variational data assimilation methods, and (iii) a simplified representation of physical processes in the atmospheric
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of industrial processes. The Department Process Diagnostics develops highly precise measurement techniques, e.g., ultrafast X-Ray tomography, ultrasound techniques and inductive methods, to characterize
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 1 month ago
developed. Subsequently, experimental and numerical studies are foreseen on materials that are more difficult to use in L-PBF see more here https://www.cemef.minesparis.psl.eu/wp-content/uploads/2026/01
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Posting Details Student Title Classification Information Quick Link https://chapman.peopleadmin.com/postings/39133 Job Number SE179024 Position Information Department or Unit Name Fowler School
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accepted all year round Details The advent of easily accessible high performance computers or computer clusters and numerical techniques such as finite element methods (FEM) facilitates the highly accurate
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the Program or a related area of the mathematical sciences, with prior familiarity with the basics of quantum algorithms. Knowledge of numerical methods for solving differential equations is an asset but not a
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data