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Field
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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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probabilistic modelling. The focus will be on quantifying and evaluating uncertainty in both numerical and categorical predictions derived from medical reports, and on integrating these uncertainties
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Law, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods
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predictive models, and interpreting large environmental datasets, collaborating in interdisciplinary projects and in the production of scientific publications. In the performance of duties, it may sometimes be
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workflow that maps first-principles electronic-structure data onto predictive atomistic spin-Hamiltonians and device-scale dynamical models. The candidate will run high-throughput, relativistic DFT
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through microstructural characterization and comparing experimental observations with thermodynamic model predictions. Publishing your findings in peer-reviewed international journals and presenting
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observations with hydraulic models and digital twins, new predictive tools can be developed to identify increasing failure risks and support proactive monitoring and maintenance strategies for drinking water
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computing. This particular position focuses on time-series analysis and forecasting using transformer based foundation models. About the Project Time-series prediction using transformer based models is
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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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modeling, photovoltaics, high-temperature experimentation, and solar energy technologies. Thermophotovoltaic (TPV) systems convert thermal radiation emitted by a hot surface into electricity using lowbandgap