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validating uncertainty-aware AI models for real-time crash risk prediction with guaranteed confidence bounds, interpretability, and fairness-by-design, using a blend of centralized and federated learning
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on predictive performance, the research will investigate methods to balance model accuracy and computational complexity, enabling the development of more sustainable modelling approaches. The project combines
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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of this position is to assess the current capabilities of quantum computing for Numerical Weather Prediction (NWP), with a focus on developing hybrid quantum-classical or quantum-AI approaches. This includes
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Post Doctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI
patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics) • Multi-omics for non-cancer health screening applications, • Machine learning modelling