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molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials. The research
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. - Experience with large-eddy simulation (LES), high-performance computing, and numerical modeling of atmospheric flows is highly desirable. For both positions: - Ability to work independently as
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. Were seeking a creative Postdoctoral Scholar to join our team and advance cutting-edge multiscale and hybrid modelingintegrating first-principles simulations with machine learningfor chemical and
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experience in perturbative large-scale structure modeling (LSS), in particular biased dark matter tracers, in simulated dataset analysis, as well as strong programming skills in Python and C. Familiarity with
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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
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dynamics processes into the IPSL Earth System model. · Participate in the scientific exploitation of simulations performed with ORCHIDEE v4. · Collaborate with ARCHIVES project partners to ensure coordinated
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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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, the PhD candidate will either model the redistribution of a focal animal or herbaceous plant species, or simulate changes in potential animal and plant diversity at the ecosystem level. References: Chuine I
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& Modeling: Develop and simulate numerical models of damage and failure in aerospace structures, including FEA for metamaterials. Materials Design & Characterization: Study and optimize metamaterials and
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: 3D model learning, prediction models from imaging and molecular data, model-based simulation coupling, and uncertainty-aware outputs for lab/clinical validation. Work with 3D datasets, time-series