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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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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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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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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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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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accès privilégié à l'instrument THEMIS, ainsi que du temps machine alloué à l'équipe sur les calculateurs nationaux de haute performance (par exemple Jean Zay @ IDRIS, ADASTRA @ CINES) pour réaliser les
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therefore be part of a particularly dynamic research effort. As part of the JET2SB project, funds have been allocated for: - the acquisition of the computer equipment necessary for the research work
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-Performance Computing for Exascale" contributes to the design and development of numerical methods and software components that will equip future European Exascale and post-Exascale machines. This program is