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expertise in developing 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
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counting, or similar areas. Additional experience in the theory and practice of machine learning would be an asset. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8188-MARHEC-007
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will also support the development of machine-learning models to predict biochar catalytic performance from feedstock composition and preparation conditions. Where to apply Website https
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will investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable
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the complete event reconstruction and systematic uncertainty estimation pipeline for Super-Kamiokande. Reduction of Super-Kamiokande systematic uncertainties using machine learning, control samples
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will 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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integrative approaches at the interface of (epi)genomics, machine learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop
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, their photoluminescence properties have been less thoroughly explored. In this context, the AI-Unclon project funded by the ANR aims to use machine learning tools to predict the evolution of
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Information Additional comments Candidate Profile and Required Skills: Background in AI: Strong academic background or training in artificial intelligence and machine learning / deep learning. Technical
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modeling framework that combines physical glacier modeling with physics-informed machine learning using differentiable programming (Universal Differential Equations). Using the ODINN.il glacier model