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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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are seeking a candidate holding a Master 2 degree in computational biophysics, structural bioinformatics, or a related field. Knowledge of statistical mechanics and/or machine learning would be an asset
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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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) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical
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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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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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computational fluid dynamics. • Experience in modeling, uncertainty quantification, or statistical methods. • Experience in data science or machine learning is considered an asset. • Experience with high
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remains open for the later stages of the PhD. During this PhD, the student will acquire highly valued skills in climate modeling, climate simulation analysis, and will contribute to expand the knowledge