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mitigation strategies, such as selective yaw control and power down-regulation of wind turbines. Use experimental, field, and numerical data to validate, improve, and develop fast analytical wake models
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Description One or possibly two postdoctoral positions to work on the theory of generative AI—particularly diffusion models and their generalizations—combining methods rooted in statistical physics and
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Numerical simulations Analysis of experimental data Laboratoire Jean Perrin Theory group Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR8237-RAPVOI-006/Candidater.aspx Requirements Research
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. of Mathematical Modelling and Numerical Analysis. The position is funded through the Marie Sklodowska Curie Doctoral Network ACHILLES, which brings together a broad spectrum of expertise, including basic research
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experiments in gelatine (https://doi.org/10.5194/egusphere-egu25-9211). This model will be tested with analogue experiment results from the MAGMA Lab, run by other members of the project team. The second
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degree involving CFD, physics and/or numerical modelling Specific Requirements Programming skills (Python, C++) LanguagesENGLISHLevelExcellent Additional Information Benefits IFP Energies nouvelles is a
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and good knowledge of opensource development practices; • solid understanding in at least one of the following areas: numerical methods, computational modeling, machine learning or continuum mechanics
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, and/or numerical mathematics, as well as an excellent command of a programming language, preferably Python or C/C++. The candidate should have an interest in modeling and solving a complex, coupled
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laboratory. The candidate will benefit from the expertise in numerical simulation and bioinformatics available at the L2C and the IBMM. The IBMM will produce the initial glycoproteins for calculations using
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of errors between model predictions and post-operative reality This work will be carried out by the Biomécamot team (https://www.timc.fr/BiomecaMot ) at the TIMC laboratory, which is part of the CNRS's