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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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experimental campaigns to acquire the data required for his thesis, as well as for the MONI-TREE project as a whole. The PhD will take place in LETG-Rennes, with expected missions to ONERA's Palaiseau for works
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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 Research ExperienceNone Additional Information Additional comments Programming skills in Python/C++ are expected, as well as an interest in research, machine learning, bio-inspiration, electronics, and
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machine learning, speech science, cognition and behavioural studies. If all disciplines are addressed in this PhD position, candidates without expertise in some of the areas listed below are nevertheless
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is an impossible task in metasurfaces composed of millions of elements. The PhD aims to develop methods using machine learning surrogate models to solve this complex design problem. The project aims
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, control, machine learning. Requirements Research FieldEngineeringEducation LevelPhD or equivalent Research FieldComputer scienceEducation LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD
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of this project are expected to be published in the main natural language processing conferences/journals (*ACL/EMNLP/TACL) and/or main machine learning conferences/journals (NeurIPS/ICLR/ICML/AISTATS/TMLR
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-dimensional Euclidean spaces - Machine learning - Distributed computing - Complexity (communication, queries, memory) - Continuous optimization The specific topic can be refined based on the candidates' skills
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, etc.). He/she will benefit from the site's infrastructures and services (computer equipment, on-site catering). Professional travel is to be expected (work meetings and field missions). Learning from