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Grenoble Website http://www.grenoble-inp.fr Street 46 avenue Félix Viallet Postal Code 38000 STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket Viadeo Gmail
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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Génétique CellulairesCountryFranceCityBORDEAUX Contact City BORDEAUX Website http://www.ibgc.cnrs.fr STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket Viadeo
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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
and broaden the scope of geophysical models. We will develop a neural solver that dynamically learns to solve the momentum conservation equations. Unlike traditional supervised learning approaches, our
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. Depending on the candidate's profile and interests, the thesis may develop along one or several of the following directions, at the crossroads of statistical physics, biophysics, and machine learning
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
/hal-04213978 Pierre Cesar, Sofya Dymchenko, Abhishek Purandare, Bruno Raffin. *Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training*. 2026. https://inria.hal.science
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available1Company/InstituteBRGMCountryFranceGeofield Contact City Orléans cedex 2 Website https://www.brgm.fr/ Street 3, Avenue C. Guillemin BP 6009 Postal Code 45060 E-Mail [email protected] STATUS: EXPIRED X
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d'architecture des systèmesCountryFranceCityTOULOUSE Contact City TOULOUSE Website http://www.laas.fr STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket Viadeo Gmail
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods