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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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emerging types of national emergencies and evaluate their spatial and operational implications. This will include an analysis of UK population distributions, terrain, infrastructure access, and airspace
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Project advert Madagascar is a globally important biodiversity hotspot, home to hundreds of endemic species. At juxtaposition with this rich natural heritage is a growing human population and a
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architectures and distributed storage integration. Examining the physical arrangement, fire safety, redundancy, and maintenance requirements for embedded storage. Evaluating economic considerations, including
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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persists, even for the most powerful sensors operating in this way. A drastic departure from this sensing architecture is “multistatic” radar – enacted by a coherent network of spatially distributed sensors
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achievement of net-zero greenhouse gas emissions targets. Because of this, peatland restoration is a conservation priority in the UK and internationally. However, as ecosystems dependent on the maintenance
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Location: Students will be based at the CREWW building on Streatham Campus in Exeter. Flooding is the most common natural disaster, impacting billions worldwide. Natural Flood Management (NFM), a
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computational tools to support the safe and ethical deployment of AI in clinical settings. The research focus is on AI performance monitoring, distribution shift detection, bias assessment, and stress testing
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A PhD studentship is available to work on Logistics automation. The student associate will work in the Intelligent Logistics Group within the Distributed Information and Automation Laboratory (DIAL