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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between
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transfer learning to transpose the recurrent neural network (RNN) model available for supercritical CO2 power cycles to other cycles. Since thermodynamic conditions vary greatly depending on the fluid and
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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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of Communities team and interact with its members. The modeling work will also involve collaborations with researchers from CEFE (Montpellier), BIOGECO (Bordeaux), and forest management partners (ONF). Our little
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of the landscape over time. The LANDIS-II forest landscape disturbance and succession model will be used to perform simulations based on palaeoecological data. The student will collaborate with project researchers
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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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, 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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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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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