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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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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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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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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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: - 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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, 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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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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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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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