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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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: 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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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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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
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-on-chip platforms. 🤝 Why join us ? …. By joining the IRIG Institute, you will become part of a dynamic and innovative research environment where you will have the opportunity to learn, grow and play a key
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used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore