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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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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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clothing and its ability to build the specifications of functionalized textiles, integrating the constraints of existing military and technical textiles. For more than ten years, the world has experienced a
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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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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 10 days ago
access to GRAME-CNCM (computer music center based in Lyon) facilities, fostering potential collaborations with artists, etc. References [1] Gregorio Giudici, Franco Caspe, Leonardo Gabrielli, Stefano
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problems of on-device learning for spintronic devices, proposing and impl menting technical solutions and communicating his scientific results Where to apply E-mail job-ref-waft5vlowa@emploi.beetween.com
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via thermodynamic power cycles. However, conventional expansion machines (turbines or volumetric devices) face significant limitations at low power scales: - Turbomachinery suffers from reduced
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation