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Website https://emploi.cnrs.fr/Candidat/Offre/UMR7107-ANIFOR-019/Candidater.aspx Requirements Research FieldLanguage sciencesEducation LevelPhD or equivalent Research FieldLanguage sciencesEducation
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the use of reinforcement learning approaches to enable tractable active auditing, by both relaxing guarantees and by adding work assumptions for proposing efficient algorithms. Where to apply Website https
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topic, the work addresses exploration of new concepts and technologies (in particular for reusable launch vehicles), and methodological research which includes MDO, surrogate modelling, deep learning and
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the context of crisis management. Particular attention will be paid to estimating the dynamics of groups of individuals. The research will seek to develop and evaluate physics-based learning (PINN) approaches
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, Bash, R/Julia, C++, or similar) and in modelling natural systems, as well as a strong motivation to learn new programming languages relevant to this field. - A solid understanding of the concepts
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learning. Work carried out during the Master's internship has already identified strong trends and tested statistical and machine learning approaches. The thesis will aim to consolidate and update
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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forces on each mode in order to reduce (i.e., cool) their individual vibrations. The student will be closely guided by the advisor and will acquire both theoretical and experimental skills on optomechanics
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behavior. (2) Evaluate their effects on performance, safety, and security metrics. (3) Propose and validate mitigation and hardening techniques at the model, system, and learning levels. The targeted
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simulations, optimisation, machine learning and turbulence modeling. The researcher must hold a Phd in fluid mechanics / Applied mathematic / Machine Learning. Website for additional job details https