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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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chronic monitoring and facilitate seamless integration into dynamic neuromodulation systems for closed-loop therapeutic interventions. By leveraging graphene’s exceptional electrochemical properties with
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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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 University of Exeter has a number of fully funded EPSRC (Engineering and Physical Sciences Research Council ) Doctoral Landscape Award (EPSRC DLA) studentships for 2026/27 entry. Students will be given sector-leading training and development with outstanding facilities and resources. The...
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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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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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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and Innovation rates Payment of university tuition fees The budget for project costs is £9,000 which can be used for computer, lab, and fieldwork costs necessary for you to conduct your research
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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