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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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project findings at international conferences Co-organize the project conference Disseminate the results among the wider public Participate in PhD training and project meetings (Co)-teach courses at the BA
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Participate in PhD training and project meetings (Co)-teach courses at the BA-level in the 2nd and 3rd year of the appointment for 0,2 fte per year Complete your PhD thesis within the required timeframe of 4
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 21 days ago
Python and good analytical skills. A good background in probability/statistics and deep learning is expected. Knowledge of differential privacy and/or fairness is a plus, but not necessary. The candidate
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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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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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-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