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Deep Learning-type methods. The focus will be on geodesic methods, the search for paths of minimum length according to an adapted metric, imposing for example a penalization of the curvature. In addition
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will focus on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural
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This position will consist of using "deep learning" methods, in particular CNNs and "transformers" for the processing of data from the IASI instrument from CNES. These observations are brightness temperature
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processing, involving machine learning techniques, as well as active participation in data collection from the detectors deployed on site. - Analysis of particle physics data applied to muography: filtering
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» Computational chemistryYears of Research Experience1 - 4 Additional Information Eligibility criteria - PhD in theoretical chemistry applied to materials, materials physics, computer science/applied mathematics
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team within the GrAMM group, which comprises two associate professors, a research director, and two CNRS research scientists. The group current-ly hosts two PhD students and two postdoctoral researchers
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knowledge of the rules governing microbial assemblages in order to propose practical solutions for improving wetland management and governance. The specific objectives of the MAEWA project are to acquire new
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ejection (CME) impacts, but also outside CME periods, when plasma jets are detected. It will involve developing a machine-learning detection tool to extend the event databases corresponding to conjunctions