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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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collaborations to implement cosmological analyses derived from the observation of Type Ia Supernovae. This work, carried out within the framework of the ANR SCINF project, aims to develop inference methods based
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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