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Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about
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observations – to develop statistical tools that integrate multiple data streams into indicators of individual health. The project will interface directly with management agencies (NOAA, WDFW, DFO), and the
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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be given sector-leading training and development with outstanding facilities and resources. The EPSRC studentships will be offered across the whole of EPSRC’s remit , with projects at the interface
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Project details Objective: This project aims to develop a wireless, nanoengineered graphene-based biosensor for real-time dopamine (DA) detection. The wireless design of the sensor aims to enable
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functions). Explore model-based RL approaches that integrate learned models with planning and adaptation mechanisms. Hybrid Evolutionary-RL Framework Develop novel frameworks with evolutionary algorithms
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functions). Explore model-based RL approaches that integrate learned models with planning and adaptation mechanisms. Hybrid Evolutionary-RL Framework Develop novel frameworks with evolutionary algorithms
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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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person. Aims: The project aims to develop and evaluate AI methods for medical image analysis to detect diabetic retinopathy, glaucoma, cataract and age-related macular degeneration (AMD). As
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
: Algorithm Validation and Use Case Demonstration (Months 27–36): This WP will first develop an integrated hardware–software testbed to systematically validate the performance of proposed solutions under