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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
simulations are a key tool for testing scientific hypotheses and predicting the evolution of these systems. However, their computational cost remains prohibitive, particularly at large spatial and temporal
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between pollution control efficiency, electrochemical yield, and energy recovery potential; • Develop coupled electrochemical and hydrodynamic models to predict process behavior; • Participate in
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
phenomena. New meta-model architectures based on learning may be proposed and tested on complex EDF use cases. However, this is not sufficient: can such a surrogate, learned from simulation data, predict
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will rely on: Developing machine learning-based surrogate AI models (physically informed neural networks) to predict the evolution of calcium carbonate precipitation rates associated with the reduction
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bosons at high energies, thereby preserving perturbative unitarity. Any deviation of the Higgs boson couplings from their Standard Model predictions would require the existence of new interactions
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know how to predict these catastrophic events. Nevertheless, earthquake and tsunami early warning systems exist. They rely on the fact that seismic waves and tsunamis propagate slower than
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collaborate with the G2Elab modeling team on numerical models of quench dynamics. This development will aim, in the long term, to enhance our ability to predict quench behavior, as well as to serve as a tool
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Framatome's Research Center (DTI). **PhD Programme** The objective of this PhD research is to develop a wear model capable of predicting wear kinetics under variable impact-fretting loading conditions for a
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skills by providing high-performance perception, decision and control functionalities based on AI model outputs that are controlled to be dependable and trustworthy. The selected candidate will join the
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effects, cyclones, the intensification of human activities, and associated disturbances (e.g., mining). On this basis, predictive models using artificial intelligence tools will be developed to anticipate