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the Mechanical Engineering department to teach on the engineering degree programme, this covering the core curriculum (first and second years L1, L2) and, potentially, the Master’s programme (M1, M2). He/she will
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sites. Teaching will take place on several sites. Research The person recruited should be able to join LaRAC, the Laboratory for Research on Learning in Context (Laboratoire de Recherche sur les
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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and our Department is available at https://www.essec.edu/en/pages/departement-information-systems-data-analytics-operations/ ESSEC BUSINESS SCHOOL holds the HR Excellence in Research award. As part of
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. Applications must include a CV, a cover letter, and transcripts from Master's 1 and 2. Title : Deep-learning for nuclear data in physics for health This PhD project aims to improve the modeling of nuclear
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scientific supervisor. Role: This position offers a strong opportunity to contribute to applied research at the intersection of economics and deep learning. It is research-intensive, requiring a high degree of
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particle validation; (2) the robustness of learning models, which is strongly dependent on data quality, representativeness and evolution, particularly with the emergence of new ammunition types and lead
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Laboratory (Laboratoire de Psychologie et NeuroCognition, CNRS UMR 5105), whose research focuses on the psychological and neurocognitive mechanisms of cognition, development, and learning. They may also join
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
research subject:https://www.defrost.inria.fr/project/horizon-europe-ire-2024-2028/ (project page) and https://www.defrost.inria.fr/research/publications/ (DEFROST team publications) Collaboration