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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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and validated within the PhLAM team. Consequently, only limited effort will be required to acquire the experimental skills needed for this part of the project, allowing the PhD candidate to focus
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of the century, which could be further amplified by a slowdown in the Atlantic Meridional Overturning Circulation (AMOC). This PhD is part of the French-Brazilian project AMACLIM (https://www.lsce.ipsl.fr/anr
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of technological disruption driven by Artificial Intelligence, we propose to analyze the data and quantify these similarities by exploring various applications of machine learning methods. With the advancement of AI
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member of the French metrology network FIRST-TF, and a member of the REFIMEVE+ project, which physically links our institute to the LTE laboratory in Paris. This thesis will allow the candidate to acquire
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development have been set-up to respond to these societal issues. Scientific supervisor: Thomas Berthelon - MOST team ([email protected] ) Where to apply Website https://emploi.cnrs.fr