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with Simulation-based Inference ), funded by the Multidisciplinary Institute for Artificial Intelligence (MIAI) through an AIforScience Research Chair. ADACSI will develop a multi-probe simulation-based
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the simulation of turbulent flows using a tensor network representation of the Navier–Stokes equations. Unlike recent approaches based on tensor networks, which simulate fluid flows in physical space using finite
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Théorique (LAPTh, CNRS/Univ. Savoie Mont Blanc) invites expressions of interest for one or more postdoctoral researcher positions focused on advancing simulation-based Inference (SBI) frameworks for modern
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team, where the agent will work under the supervision of a CNRS researcher and interact with researchers and students at different levels, including BTS, Bachelor's, Master's and PhD students. The agent
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-Chem simulations for modeling aerosols and clouds in the Arctic region. - Analysis of the interactions between aerosols, clouds, and climate using data from the simulations. - Validation of the models
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part of a research project dedicated to the development and characterization of Antibody-Radionuclide-Drug Conjugates (ARDCs). ARDCs represent a new generation of therapeutic agents combining
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program on Working Package 3, which focuses on a demonstrator illustrating the application of the mathematical theory of multi-agent viability in monetary economics. Designing a Model for Applying multi
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numerical simulations and analytical calculations, (iii) data analysis by programming, e.g. python and matlab, (iv) help and guide of Ph.D students in the team, (v) writing research reports, publishing
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, theoretical modelling and numerical simulations. The team focuses on model materials with controlled physical parameters, enabling quantitative comparisons between experiments, models and simulations, with
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption