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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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. The project proposes an innovative approach to model sea ice dynamics from the ice floe scale to the basin scale, leveraging hybrid data assimilation and machine learning methods to shape a physically robust
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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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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