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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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project aims to develop digital polymers capable of storing binary information at the molecular level. To achieve this, we will synthesize sequence-controlled polymers using a multi-step solid-phase
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, plasma polymer growth mechanisms, and the physicochemical properties of the resulting coatings, and for transferring the optimized conditions from model surfaces to magnetic nanoparticles. The main
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Skills and Qualifications • PhD in Chemistry, Physicochemistry of Polymer Materials, or a related field. • Strong experience in polymer characterization. Knowledge of bio-based materials is a plus but not
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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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and fabricating transparent and flexible microelectrode arrays (MEAs) based on conductive polymers for recording the electrical activity of enteric neuronal networks, as well as for performing cellular
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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