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optimizing a superstructure automatically generated using a deep learning approach. The project brings together the LRGP (Reaction and Engineering Laboratory, CNRS-Université de Lorraine), the LPSM
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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(Technology for engagement and personalized support of people with aphasia in rehabilitation). The candidate will join a research team with extensive experience in ergonomics and HCI, particularly in the design
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parameters to identify regimes that ensure both flame stability and low pollutant emissions. Machine learning techniques have recently shown promise for Design of Experiments (DoE) and interpretation of large
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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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, 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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particular NLP, statistical learning, machine learning, generative AI, and their major fields of application. Roles and responsibilities The applicant will join the team of the 3IA Côte d’Azur Institute and
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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will
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an internationally recognized research team at LAAS-CNRS in Toulouse, focused on developing autonomous mobile machines that integrate perception, reasoning, learning, action, and reaction capabilities