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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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, 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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Details The aim of this project is to combine nanomechanical methods with modelling (i) to develop quantitative, predictive models for the behaviour of molecules in sliding contacts, and (ii) to understand
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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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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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datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and
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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
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of concrete samples by alternating short-term model predictions and accelerated aging experiments on reconstructed aged-equivalent samples. The methods to develop and adopt will be: for O1, literature review
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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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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