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project entitled “A continually-learning framework for uncertainty quantification and translation of preclinical studies to human cardiovascular safety”. The aim of the project is to develop a statistical
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) Development of workflows and methods enabling AI-powered decision assistants to support full human operators control under risk and model uncertainty, and considering human-AI co-learning.; 2) Develop
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
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datasets. Develop quantitatively predictive models of biological systems. Integrate multi-omics data into quantitative computational models. Apply Monte Carlo sampling approaches to quantify uncertainty
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do not prove the requirements required in this public notice are excluded from admission. In case of doubt, the evaluation panel may demand any candidate to present documents proving those statements
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incorrectly or who do not prove the requirements required in this public notice are excluded from admission. In case of doubt, the evaluation panel may demand any candidate to present documents proving those
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. Candidates who submit their application incorrectly or who do not prove the requirements required in this public notice are excluded from admission. In case of doubt, the evaluation panel may demand any
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reliability. ; ; The research will explore graph-based representations of endoscopic examinations, anatomically structured learning, uncertainty estimation, and confidence-aware aggregation strategies, enabling
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of reactor transients and quantify how surrogate uncertainties propagate through system safety analysis of nuclear installations, using tools such as open simulations platforms. This research will be part of a