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numerical models, kept on track with satellite data using ensemble data assimilation methods. But ocean forecasting research is also adopting ML gradually, slower due to the lack of ocean observations
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 4 days ago
investigations focused on spin dynamics, · Perform numerical simulations to study: - ultrafast magnetization phenomena, - interactions with surface acoustic waves, - propagation of sound waves in the presence of a
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particular, it should relate to one or more of the following areas: theory for spatial discretisations of PDEs, analysis and design of domain decomposition methods, numerical analysis for stochastic PDEs
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Institut Supérieur de l'Aéronautique et de l'Espace | Toulouse, Midi Pyrenees | France | 19 days ago
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datasets (e.g. ground-based radar measurements and weather station data) Contribute to the development and application of numerical models Assess uncertainties for future sea-level projections Publish
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research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization. Some knowledge of probability and
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computing environments. Experience with numerical modelling techniques, such as finite difference, finite element, or spectral element methods. Interest in inverse problem formulation and solving and/or
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to the design, implementation, and scientific exploitation of numerous satellite missions. CESBIO is a Unité Mixte de Recherche (Joint Research Unit) bringing together the CNES, CNRS, IRD, and the University
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, engineering, applied mathematics, physics, or another STEM discipline. · Demonstrated experience with mathematical and numerical optimization methods, including, but not restricted to, geometry or shape
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 2 months ago
-Carrillo et al. 2024). Champaney et al., 2022. Int J Mater Form 15, 31. https://doi.org/10.1007/s12289-022-01678-4 Chinesta et al., 2020. Arch Computat Methods Eng 27, 105–134 https://doi.org/10.1007/s11831