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the BENEFIT project, funded by the French National Research Agency (ANR), to work on active flow control and machine learning. 1- CONTEXT -------------------- Active flow control aims to modify velocity fields
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Contributions - A formal framework for modeling well-being in adaptive learning - A well-being-aware student simulation model - A multi-objective RL framework - An orchestration architecture - Experimental
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the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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on the interaction between human cognition and language—understood as a cognitive entity, a means of communication, an object of learning and lifelong development, and a sociocultural phenomenon. Mission : Background
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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expertise in HCI and education, including adaptive gamification, engagement, learning analysis, and the design of motivational affordances in education. As part of the project, the PhD student will work with
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objective will be to determine whether such exposure generates toxic effects and/or triggers the epithelial-to-mesenchymal transition (EMT), a process by which sedentary epithelial cells acquire invasive
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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 advisors and will acquire both theoretical and experimental skills