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practice of machine learning would be an asset. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8188-MARHEC-007/Default.aspx Work Location(s) Number of offers available2Company
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required. •Experience with deep-learning techniques and libraries welcome. •Past experience in neutrino oscillation and/or LAr TPC experiment welcome. Website for additional job details https
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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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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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computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work
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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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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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imaging setups, XRF scanners, micro-XRF, and synchrotron XANES), as well as guidance at the intersection of paleontology, biology, and physical chemistry. Where to apply Website https://emploi.cnrs.fr
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical