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sciences, particularly in the domain of numerical weather prediction. On the one hand, state-of-the-art models such as GraphCast have demonstrated outstanding predictive skill. On the other hand, physics
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of electrical topologies (AC, DC, three-phase) and will integrate both standard and atypical wear cases. On this basis, high-performance artificial intelligence models will be developed. By combining neural
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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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to modern genomic datasets that may involve hundreds of populations. He/she will also develop probabilistic GO models inspired from the Redundancy Analysis approach and extend it by introducing Neural
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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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environment featuring a wide range of research activities, including QM/MM simulations, ionic liquid simulations, and excited-state characterization. The aim of this PhD thesis is the atomistic modeling
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of different radiation models on the prediction of laminar burning velocity will be systematically analyzed. Sensitivity analyses of chemical kinetics and the associated uncertainties will be
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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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intelligent decisions. In particular, when low latency is critical, these models are deployed directly on devices at the edge, close to the equipment itself — a necessary condition for smarter, faster