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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/12 11:59PM ** (posted 2025/10/21, listed until 2026/04/21) Position Description: Apply Position Description
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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Applied Mathematics, Computer Science, or Theoretical Physics (at the time of appointment). Background in machine learning theory or in one or more of: high-dimensional probability, random matrix theory
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
research visits, fostering the dissemination of the findings and collaborations within the academic community. The research topic focuses on fundamental developments of a novel learning framework for
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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Networking (SDN), and advanced machine learning approaches to ensure compliance with ISO/SAE 21434 and UNECE WP.29. Respnsibilities: Develop lightweight AI/ML models for detecting cyber threats in TSN-based