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compare the patterns modeled, and demonstrated the relevance of these approaches by integrating them in a brain morphometric method based on machine learning [3]. Recently, we started to explore
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Research Project: Implementation of Machine Learning techniques to optimize laser-plasma accelerators experimentally and numerically for industrial and medical applications. Since the first
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: - Apply unsupervised machine learning concepts to the analysis of continuous seismograms recorded in the vicinity of active volcanoes, in order to extract information about the state of the volcano and the
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objective will be to develop algorithms for predicting and planning the evolution of local energy systems (microgrids) over a time horizon of several years, using machine learning and numerical optimization
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, integrating machine learning and physiological computational models (patient digital twin) to: 1) combine physiological knowledge and clinical data; 2) improve model interpretability; and 3) minimize
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The development of statistical/machine learning approaches for downscaling at the kilometer scale will be the main mission of the position. For various climate variables (temperature, precipitation, wind, etc
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mechanical activity at the same time. In this context, the use of mathematical models and machine learning methods can be relevant to integrate physiological knowledge in data analysis and to analyze
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specialized in Technology-Enhanced Learning (TEL) and Human-Computer Interaction (HCI). In particular, SICAL has extensive experience in behavior analysis using multimodal data in different contexts, including
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professors, teacher-researchers, engineers, and doctoral students. The team is mainly involved in the PALLAS laser-plasma accelerator project. Optimizing Laser-Plasma Accelerators through Machine Learning The
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of Inria and will be supervised by Nikolaos Georgantas ([email protected] ) and Maroua Bahri ([email protected] ). Mission confiée Automated Machine Learning (AutoML) is an approach