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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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close coordination with project partners, the recruited researcher will conduct experiments to determine the extent to which neural models, now at the heart of many approaches to Natural
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. The models will be used to predict dynamic responses to stressors, sleep disruptions, and diagnostic tests, as well as the long-term changes that occur during disease. A key challenge of your work will be
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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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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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: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis
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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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supports tasks such as predictive modeling, anomaly detection, and synthetic data generation. The models developed are expected to exploit metadata to guide and condition image analysis outputs. By