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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
is to exploit transformer-based attention mechanisms to model sequential dependencies and capture long-range interactions, making them promising tools for complex spectrum management. Despite being
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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
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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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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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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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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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. This project aims to dive into the dynamics of attack methodologies (e.g., Membership Inference, Property Inference) and defensive mechanisms (e.g., Differential Privacy, Machine Unlearning) within FL