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of Higher Education and Research (MESR). PINNACLE: Physics-Informed Neural Networks for Accelerated Cloud Light-Scattering Emulation Artificial intelligence is profoundly transforming atmospheric
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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respect to an infinitesimal perturbation of the dataset, provide a rigorous framework to: - **Identify the most informative samples** among the predictions of a deep neural network (DNN), with the goal
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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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approaches that build processes by mutation operators [1], natural language processing techniques with recurrent short-term memory (LSTM) neural networks [2], and variational autoencoders (VAE
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Tubes via Recurrent Neural Networks for Planning Robust Robot Motions". In ECAI 2024 (pp. 4385-4392). IOS Press. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UPR8001-MARCOG-003