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-scale simulations from the PEPR BRIDGES Conduct sensitivity studies based on simulations in which the coupling between the ocean, the atmosphere and waves will be gradually degraded (as done e.g., by
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of the Inria center of the University of Lille. The position is funded by the CPER WaveTech@HdF project. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8524-GUIFER-001/Default.aspx Requirements
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fundamental questions in statistical physics. The work is primarily numerical, and we seek candidates with experience in particle-based simulations (e.g., molecular dynamics) and a strong interest in large
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of wood formation (CEMACam project). - main mission: The candidate will be tasked with continuing the development of a wood formation simulation model initiated during the project. This 'vertex' model
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Post-doctoral position (M/F) for testing drought-based BEF relationships at CEFE Montpellier, France
) Carry-out additional simulations with the Phoreau model to test the effect of tree diversity on forests' resistance to droughts. ii) Analyse biodiversity-drought resistance relationships, across a
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of the UltiMatePV project and will coordinate tasks with the project's various partners. Participate in the technological maturation of devices. Conduct numerical simulations (optoelectronic devices, transport
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correlations. - Analyse experimental data mixing both quantum optics and solid-state physics phenomena - Supervise PhD students and interns - Ensure an efficient collaboration and information sharing with
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the effects of these factors at the tissue level. The position will also involve simulating and optimizing complex models that integrate biological and physical data using advanced methods, such as physics
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code; • Simulating spallation source transients in MYRRHA and EFIT and reactivity measurements using various types of detectors; • Comparing the space-energy effects calculated by simulation in MYRRHA
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quantification of pigments and binders. The work will include the generation of simulated spectral data based on physical models, the training and optimization of machine learning models, and their validation