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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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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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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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in Artificial Intelligence (Machine Learning and Statistics) at CentraleSupélec, · Joël Eymery, Head of the Nanostructures and Synchrotron Radiation Team at CEA Grenoble, · Jean-Sébastien
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(History, Archeology, …). Expected skills: The candidate should have a graduate degree (Master 2 degree). His/her scholar background should include: • statistical/machine learning, statistical inference
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Skills/Qualifications Strong background in Operating Systems and Linux development Knowledge of memory management mechanisms and system-level programming Experience with Machine Learning models (design
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of and experience with hydrological and/or hydrogeological modeling - Knowledge of and experience with AI concepts (machine learning, deep learning, and PINNS) and/or digital twin development - Experience
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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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by exploiting foundational machine-learning potentials such as MACE, SevenNet, or Orb-V3. The predictions will then be progressively refined and verified by DFT and, ultimately, tested experimentally