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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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, machine-learning tools, and Lagrangian transport modelling. You will be based at the British Antarctic Survey and work closely with experts at the University of Leeds and Exeter, who provide cutting-edge
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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well as retinal fundus images, we will explore analysis of new eye image datasets including OCTA and CCM images for diagnosis of diabetic neuropathy Machine Learning: We will develop artificial intelligence (AI
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multimodal satellite Earth Observation and machine learning can be used to quantify cyclone and storm damage in plantation forests. The core focus could be on integrating pre-storm LiDAR with post-storm
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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the central challenge hindering this vision: the fundamental incompatibility between text-native LLMs and the operational reality of computer networks. Directly applying LLMs is impeded by three core technical