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, durability, and environmental sustainability, while addressing cost constraints and net zero objectives. It will include an in-depth review of shortcomings in current design, based on literature review and
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
resources. To fill this gap, this proposal aims to design novel distributed and lightweight LLMs for spectrum management in aerial 6G networks. Specifically, the project will design wireless-aware data
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how best to balance competing objectives. We will also investigate architectural design strategies that implicitly encourage constraint adherence, such as averaging features across satellites. In
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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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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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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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interdisciplinary expertise: biosciences to characterise the physiological responses of mussels under controlled exposures, and engineering to design hardware, firmware, and analytical pipelines that can run
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the Southwest. Geospatial and engineering analyses will identify optimal sites and system configurations, while collaboration with the Law School will assess legal and regulatory frameworks, planning constraints
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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