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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 12 days ago
, based on the simulated microstructures. The ATALANTE project, of which this PhD is a part, is divided between experimentation, modeling, and commercialization, and brings together five partners: CEMEF
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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models to build customized AI pipelines for tasks such as image enhancement, detection, segmentation, and robotic control. You will contribute to both the backend development (pipeline orchestration
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, pour fault tolérant control) sont des enjeux primordiaux. En effet le diagnostic permet de détecter, localiser et éventuellement quantifier un ou des dysfonctionnements dans un procédé. La commande
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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Controlling pollutant emissions is one
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protein or nucleic acid biophysics and structural characterization Experience with computational modeling and data analysis Excellent command of English, written and spoken Additional qualifications
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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-contractuel-fh-en-diagnostic-des-batterie… Requirements Research FieldChemistryEducation LevelPhD or equivalent Skills/Qualifications Skills Good knowledge of physics-based modelling and experimental skills. C1
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, creating predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis