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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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be responsible for designing and implementing physics-informed machine learning strategies for identifying constitutive laws in granular media. This includes the development of thermodynamically
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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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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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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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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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the cosmological analysis of LSST Y1 within the LSST DESC collaboration. Integrate the tools into the LSST pipelines and validate them on volumes representative of the survey; document and distribute the open-source
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As machine learning systems become increasingly
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analytical studies. -Investigate transport, localization and fluctuation properties of the systems under study. -Compare theoretical predictions with experimental results in collaboration with partners
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journals and presentations at conferences. - Collaboration with interdisciplinary research teams as part of the CERTAINTY project. - Optimization and documentation of modeling configurations for future