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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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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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optics, biophotonics spectroscopy and imaging, instrumentation - Numerical signal/data processing and analysis (MATLAB) - Machine learning - Light-tissue interaction modelling - Very good level in English
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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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molecular simulations, machine-learning techniques, and statistical mechanics for research opportunities in: Development of data-driven schemes for the discovery of slow degrees of freedom Molecular
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humaines et démocratiser la création. Vous jouerez un rôle clé dans le développement de cette solution, en recherchant et implémentant les solutions d’IA/Machine Learning innovantes qui constitueront le
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human cohorts would be a plus. Minimum level of training and/or experience required : Doctorate in Computer Science on a topic related to machine learning or deep learning Research FieldComputer science
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research colleagues, and to learn about the larger context of my research and the research project. Offer: The aim of this PhD research is to optimize acoustic metasurfaces of finite size using machine