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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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forces on each mode in order to reduce (i.e., cool) their individual vibrations. The student will be closely guided by the advisor and will acquire both theoretical and experimental skills on optomechanics
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draw upon the experience acquired by other individuals to complement its own, which allows it to save learning time and avoid the risk of harmful consequences from inappropriate behaviour. It is crucial
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professionals and school staff of the territory, in a participatory research approach. The IHES team at EBI collaborates closely with the Centre for Study and Research on Cognition and Learning (CeRCA
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authorities. École des Ponts ParisTech, in accordance with its strategic plan, develops a long-term research activity in the field of Machine Learning and Computer Vision. The IMAGINE team is a renowned
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knowledge of multi-objective problems. Master students or Engineers in the field of Process Systems Engineering are strongly encouraged to apply. Knowledge of machine learning algorithms, energy markets and
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required: The candidate should have a strong interest in evolutionary genomics and ecology; should be willing to acquire an extensive training in bioinformatics and statistical data analysis; should have
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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individuals to complement its own, which allows it to save learning time and avoid the risk of harmful consequences from inappropriate behaviour. It is crucial that these different strategies, involving varying
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will build on recent advances in machine learning for dynamical systems to extract meaningful representations of complex flame dynamics, construct prognostic ROMs, and perform data assimilation