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, this technology requires to experiment a change of paradigm to guarantee a sustainable development for the years to come. One pathway to achieve this paradigm shift relies on the development of data-driven
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an exciting research project focused on developing computational models of human performance limits and decision-making. If you are passionate about data-driven modeling, human behavior, and safe
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or Information Engineering with a focus on AI, ML, or Data Science. Research experience in machine learning, deep learning, foundation models, or multimodal systems. Scientific publications in indexed
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analyze data generated from experiments Develop and evaluate AI-driven models for real-time decision support, predictive analytics, and automated system control in agricultural contexts Apply artificial
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apply mechanistic and data-driven models, integrated with life-cycle assessment (LCA) to quantify system performance, environmental impacts, and climate resilience. Job Requirements: Master’s degree in
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computational seismology, specifically AI-driven full-waveform inversion using diffusion-model approaches. Responsibilities Perform research in AI-driven waveform inversion using diffusion models. Investigating
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detailed information on local deformation mechanisms at the microscale, while numerical simulations and data-driven approaches will enable the development of predictive models capable of linking
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contributing to the design and development of advanced models, algorithms, and AI‑driven systems for renewable energy microgrids and data centers. The Research Assistant will work closely with laboratory
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focus on translational Research, Development & Deployment which focus on specific area of the energy value chain, and a number of Living labs and Testbeds which facilitate large scale technology
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven