155 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" PhD positions in France
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, Natural Resources, Agriculture and Environment”, which are at the heart of Horizon Europe. Host laboratories: ESRF and ILL (France), FZJ (Germany), AREA (Italy), NTNU (Norway). Where to apply Website https
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SAF combustion. Recent advances have demonstrated that machine learning techniques, particularly neural networks, can significantly accelerate chemical kinetics computations. Nevertheless, most of
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 2 months ago
of material deposited, while maintaining the expected accuracy. Part of the activity will also involve implementing Artificial Intelligence (AI) methods similar to computer vision to facilitate this prediction
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characterization); interest in applied research; rigorous and autonomous; problem-solving skills; eager to learn; communication skills To apply, please send your cover letter and CV to the IFPEN supervisor indicated
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process takes place on this platform https://ecolecentraledelyon.recruitee.com: 1. Written application: CV, a cover letter, and transcripts for the current and previous academic years. One or more
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Contributions - A formal framework for modeling well-being in adaptive learning - A well-being-aware student simulation model - A multi-objective RL framework - An orchestration architecture - Experimental
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cardiovascular disease, chronic kidney disease, cancer and chronic inflammatory disease. For more information: https://www.cordis.europa.eu/project/id/101225380 Cardiovascular disease (CVD) is the leading cause
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Location(s) Number of offers available1Company/InstituteCentrale Lille InstitutCountryFranceGeofield Contact State/Province Hauts-de-France City Villeneuve d'Ascq Website https://centralelille.fr
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research. This job offer is in the form of a 36-month full-time doctoral contract. Where to apply Website https://edictproject.eu/ Requirements Research FieldPsychological sciences » PsychologyEducation
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, soil, and plants aid in the collection of real-time data directly from the ground. Based on these historical data predictive machine learning (ML) algorithms that can alert even before a problem occurs