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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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, 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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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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, 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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acquire in the course of the PhD skills in live microscopy, experimental neurobiology, genetics and behavioural work. The student will receive mentoring and have the chance to guide the research and
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well as 110 PhD students and post-docs. Thanks to Institut de chimie of Lyon it has access to many technological platforms for characterizations. The thesis will mainly take place in its building located
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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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an equally innovative interdisciplinary approach: collaboration between Experimental Mechanics and Physics (PMMH) and Computer Graphics (inverse problems at LJK and printability at LORIA). These disciplines
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computer, virtual data storage, and access to software tools for specialized data analysis (e.g., Cryo-EM data, crystallography, etc.). Structural microbiology: Exopolysaccharide secretion and host
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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