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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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composition of the fossilized tissues of the specimens studied. The postdoctoral fellow will collaborate with project partners (BGS Dijon, PPSM Paris-Saclay, the PUMA synchrotron beamline) to integrate
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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scientific articles, presenting research results on conferences, and communicating with collaborators The Institute of Electronic, Microelectronic and Nanotechnology (UMR CNRS 8520 – https://www.iemn.fr/en
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analysis of the ATLAS experiment data. The L2IT team plays a driving role within the ATLAS collaboration for the reconstruction of charged particle tracks using geometric deep learning (GDL). The person who
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these predictions through targeted atomistic simulations. They will also contribute to the writing of scientific results and to exchanges with the project collaborators. The work will be carried out
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. In parallel, the post-doctorant will be associated as co-local contact to studies of other quantum materials using his/her compensated-coil magnetization probe, in collaboration with LNCMI researchers
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. Development and integration of state-of-the-art machine learning techniques in the analysis and event reconstruction will be a major component of this work. - Characterization of silicon detection modules using
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming