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are fundamentally limited by a "one model for one task" design philosophy. This approach incurs prohibitive engineering costs and yields brittle solutions with poor generalisation to new network conditions, trapping
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-making process. Research Objectives Model Learning in Dynamic Contexts Investigate the use of reinforcement learning for constructing and updating probabilistic world models (transition and observation
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storm to use these technologies and/or visit the affected area to evaluate storm-related tree damage. Therefore, to support sales planning and the safety of foresters working in the field, there is a need
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. The ML will use 500,000 fundus images from open-source and customised retinopathy datasets. We will compare retinopathy grading accuracy by NHS clinician vs ML algorithm. This will build on Exeter’s
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-making process. Research Objectives Model Learning in Dynamic Contexts Investigate the use of reinforcement learning for constructing and updating probabilistic world models (transition and observation
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will apply nonlinear and associational (colloquially called “causal”) timeseries analysis techniques to provide a more rigorous, and more statistically significant framework for understanding
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expertise in perturbed parameter ensembles and machine-learning model integration. A CASE placement at the Met Office will further strengthen your applied training and professional network. CASE Partners
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The University of Exeter’s Department of Engineering is inviting applications for a PhD studentship co-funded by the partner Hydro International and University of Exeter Faculty of Environment
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elasmobranchs distributed in the North-East Atlantic? how might that shift under climate change? how do deep-water elasmobranchs use or associate with benthic habitats? what role do they play in deep-sea food
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to understand the drivers and dynamics of sediment transport along this highly populated and vulnerable river. Additionally, it will explore the use of prototype water quality sensors (Hydrobeans) to understand