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to generate baseline datasets for calibrating and validating predictive models of biodiversity-rich forests. Using machine learning (ML) algorithms, the Research Assistant will help predict the occurrence
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well as next-generation ecological models that take uncertainty into account. The https://leca.osug.fr (LECA) is part of the University of Grenoble Alpes and the CNRS in France. Grenoble is located close to
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, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating
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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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, numerical methods, and Earth system modeling to develop and evaluate a coupled xylem–phloem transport framework that translates multiscale physics into next-generation vegetation model schemes. Key
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of the system, including laboratory testing and/or in situ monitoring campaigns. •Proposing predictive maintenance strategies based on the collected data and developed models, w ith the aim of optimising
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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied
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oxide ores of varying quality and mineral associations. Research will focus on process design and analysis with a strong emphasis on ore characterization. At a later stage, predictive models for size
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of varying quality and mineral associations. Research will focus on process design and analysis with a strong emphasis on ore characterization. At a later stage, predictive models for size reduction and
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& Budget Strategy Institutional Analytics & Decision Support Policy, Planning & State Operations University Business Services The Modeler supports the University’s strategic, financial, and operational