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modeling, photovoltaics, high-temperature experimentation, and solar energy technologies. Thermophotovoltaic (TPV) systems convert thermal radiation emitted by a hot surface into electricity using lowbandgap
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to reduce the amount of required training data while maintaining high predictive accuracy. Methods and Techniques : Density Functional Theory, Machine Learning for atomistic modeling Location : Institut Jean
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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intelligent decision architectures, predictive analytics, and adaptive computational models that can operate in dynamic, uncertain, and high-stakes project environments. The appointee will conduct original
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challenges of learning from network traffic, (ii) train original AI models that are designed to operate precisely on such data, and (iii) demonstrate the viability in production of AI-driven solutions for, e.g
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Reactor) designs. This system constitutes a corium containment strategy in the event of a severe accident. Therefore, understanding the thermodynamic properties of the corium is essential for predicting its
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, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate ). We also began exploring how
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models
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workflows, and data engineering for mobility platforms • AI/ML for transportation prediction, system optimization, and environmental/health impact modeling • Deployment of decision-support tools for public
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-driven methods provide excellent performance under low or cyclo-stationary regimes but struggle with highly dynamic and rapidly varying conditions; conversely, model-based state observers ensure robustness