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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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. 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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sometimes struggle to effectively sustain patients' learning throughout their rehabilitation journey and may not adapt to the evolution of their abilities. Rehabilitation is a complex process that requires
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correlations or more innovative methods of multivariate analysis and we anticipate here an opportunity of using machine learning that could help in predicting properties or classifying sources. A last step will