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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
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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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from over 120 nations take on the challenges of the sciences and the arts, of research, learning, and teaching every single day. It is the joint efforts of all JGU members doing research, studying