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particular NLP, statistical learning, machine learning, generative AI, and their major fields of application. Roles and responsibilities The applicant will join the team of the 3IA Côte d’Azur Institute and
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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
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be highly interdisciplinary. Two different profiles are possible for this position: either a profile in engineering sciences or biomedical physics, with a strong desire to learn about microbiology
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correlations or more innovative methods of multivariate analysis and we anticipate here an opportunity of using machine learning that could help in predicting properties or classifying sources. A last step will
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technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in
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-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their
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interdisciplinary, and together we contribute to science and society. Your role Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms
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: ANR JCJC “NanoG4V” : ANR-24-CE51-7558 Expected Outcomes By the end of the PhD, the candidate is expected to: • Acquire solid expertise in the synthesis and advanced characterization of quantum-grade
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 9 days ago
. [8] Michaela Blott, Thomas B Preußer, Nicholas J Fraser, Giulio Gambardella, Kenneth O’brien, Yaman Umuroglu, Miriam Leeser, and Kees Vissers. FINN-R: An end-to-end deep-learning framework for fast