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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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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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, or roundabout navigation will be considered. In this work, we also aim to explore the use of machine learning approaches [1][2] to personalize the driving system according to individual driver preferences
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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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modern machine-learning techniques, will be exploited to improve the discrimination between the different polarization states. The analysis will use the complete Run 2 and Run 3 datasets collected by
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interactions between these different institutions. Research activities related to neuromorphic spike sorting, unsupervised learning algorithms, and neural signal analysis will be primarily conducted in
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production. By developing hybrid architectures combining ontologies, generative models, reinforcement learning, and uncertainty quantification, the PhD project addresses the challenges identified by ICCARE in
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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