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performing atomistic simulations with Density Functional Theory and Molecular Dynamics. Data analysis and coarse graining in order to provide parametrisations for upper scale models (Kinetic Monte Carlo and
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subsidiarity at the territorial scale," specifically through the "materials for energy storage" program. Using molecular modeling tools, the objective is to participate in the design of a single catalyst capable
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of the project is to design, model and simulate neural networks based on magnetic skyrmion nucleation and propagation. The second objective is to fabricate these hardware neural networks, characterize
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Team : We seek a researcher with demonstrated experience in perturbative modeling of LSS (in particular biased tracers of dark matter), analysis of simulated datasets, and strong programming in Python
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collaborators. The activities will include: • Electromagnetic modeling and numerical simulations (e.g., FDTD, FEM) • Design of metasurface architectures based on dielectric materials available at CRHEA
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response to climate forcings for different past climates. · Perform and analyze global model simulations. · Collaborate with IPSL Earth System model developers to ensure consistent integration
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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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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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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