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. Experimental characterization of Hall effect thrusters using combination of diagnostic techniques such as optical emission and absorption, Langmuir probes, etc. enhanced by the application of machine learning
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exciting opportunities for machine learning to address outstanding biological questions. The PhD student to be recruited will be working on the development of machine learning methods for single-cell data
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intestinal duct section. To achieve this, we will address the inverse design problem using physics-informed machine learning that consists of determining the optimal structure and material distribution
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financement Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/133293 Requirements Specific Requirements Etudiant(e) titulaire d'un Master II en Statistique / Machine Learning
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(I3S), Sophia Antipolis Hosting lab: I3S & INRIA UniCA Apply by sending an email directly to the supervisor: emanuele.natale@univ-cotedazur.fr Primary discipline: Machine Learning Secondary discipline
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Research Infrastructure? No Offer Description The PhD will take place at LAMIH-UMR CNRS 8201, Université Polytechnique Hauts-de-France, Valenciennes, France. It is part of the JCJC ANR TeCAPE project
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on food craving and health-related decision-making. To this purpose, we will use a combination of brain imaging, behavioral measures, and machine-learning techniques. Activities The successful candidate
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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 days ago
Framework Programme? Not funded by a EU programme Reference Number 2025-09322 Is the Job related to staff position within a Research Infrastructure? No Offer Description Context. This PhD thesis is part of