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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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, physics, or data science. They should have strong analytical skills in the context of machine learning and/or numerical mathematics, as well as an excellent command of a programming language, preferably
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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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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 4 hours 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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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a highly motivated PhD candidate to advance predictive, data-driven production and
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within the broader context of Industry 4.0 and is part of an ongoing initiative to enable predictive diagnostics for electrical machines. The research The increasing need for predictive maintenance in
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) to Volumetric Arc Therapy (VMAT), Stereotactic Radiosurgery (SRS), and ultra-high dose-rate (UHDR-FLASH) therapy, the need for real-time control and verification becomes critical. This PhD will further develop
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD candidate: ML-based prediction of flood types based on atmospheric and catchment attributes Are you
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– scholarship position available in the Institute of Physical Chemistry PAS within FENG.02.02-IP.05-0063/25 „Smart Engineering of Catalysts for Hydrofunctionalization Reactions: From Selectivity Control to a