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model of magmatic oceans on forming planets based on laboratory experiments. At present, high-pressure molten silicates are modelled only in a rudimentary manner, due to a lack of data on activity
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environments, and distributional shifts. The research will combine probabilistic modelling, network-based representations, and modern AI methods to enable scalable and interpretable decision-making in complex
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modeling and optimization methods for the management of multi-energy platforms integrating electricity and heat systems within. The research aims at characterizing and valorizing the flexibility potential
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, you will have the opportunity to advance scientific expertise on integrated modelling of the entire water cycle, with particular focus on real-time simulations and operational forecasting. A key element
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computational and energy costs of LLMs, this work will focus on smaller, domain-specific models. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8254-SYLDES-026/Default.aspx Requirements
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wireless system. Analyse experimental measurements using MATLAB, Python or equivalent computational tools. Compare experimental measurements with modelling or theoretical predictions generated by the wider
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concrete. About us Digital Structures with Mineral Materials is a new professorship at the TU Munich, focusing on the de-velopment of structural engineering, design, analysis and modelling methods
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propagation in tools used in Friction Stir Welding (FSW) of advanced steel pipelines. The research activities include: Development of thermo-mechanical finite element models for Friction Stir Welding tools
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deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine which features are relevant? These questions
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents