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Field
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genetics to predict breast cancer risk and tumour aggressiveness in BRCA variant carriers Digital biomarkers for enhanced AI-guided therapy in heart failure (D-BEAT) Experimental models for optimizing
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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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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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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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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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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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. The core of the methodology involves building an analytics framework for the modeling and prediction of application metrics in the CEC, where node attributes such as workload characteristics and network
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from semantic radio maps; learn which features act as reliable predictors of rewards or outcomes; associate these features with predictive models that guide decision-making; exploit such cue–outcome
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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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speech processing, a research question that is rapidly gaining in importance. The project is centred on the hypothesis that the cerebellum conveys predictions about upcoming speech sounds to the neocortex