67 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" research jobs in France
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simulations, optimisation, machine learning and turbulence modeling. The researcher must hold a Phd in fluid mechanics / Applied mathematic / Machine Learning. Website for additional job details https
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and machine learning applied to data fusion and adapt them to the field of exoplanet characterization. They will develop and maintain the FORMOSA code in coordination with the team of students working
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willing to explore the links between Algorithm Design and Machine Learning Specific Requirements Physical presence is required (remote work impossible) LanguagesFRENCHLevelGood Research
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 months ago
candidate will join the Inria centre of the University of Lille and be part of the LOKI research group, specialized in Human-Computer Interaction. It is affiliated with the CRIStAL laboratory (UMR 9189) and
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ExperienceNone Additional Information Eligibility criteria - Holding a doctoral degree in particle physics - Experience in C++ and Python programming is desired - Experience in training and using Machine Learning
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standard Python libraries for machine learning, in particular PyTorch. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR6072-DAVTSC-008/Default.aspx Work Location(s) Number of offers
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 2 months ago
attention, prediction and learning, as well as the intricate coupling between action and perception. Our research combines (1) cross-species in-vivo observations of brain electrical and neurotransmitter
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reliability. · Understanding of hardware accelerators for AI and their operation. · Familiarity with machine learning workloads (e.g., CNNs). As this is a research position, it is necessary
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difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency
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being responsible for the confocal microscope (maintenance). Resources provided (equipment, IT, etc.): office equipped with a computer workstation, equipped laboratory bench, reagents and equipment shared