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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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over space and time in response to dryland management in the context of environmental variation. The Saudi NEOM project is one of the largest ecological restoration projects worldwide, with interventions
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About the ProjectProject details: Next-generation networks are rapidly outscaling the capabilities of traditional management paradigms. While early AI/ML models offered a degree of automation, they
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Location: Streatham Campus Project details: The health of marine ecosystems is increasingly threatened by pollution, eutrophication, and harmful algal blooms (HABs), which have profound ecological
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About the Partnership This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists
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to improved resilience and evidence-based strategies for managing wildfire risks. Useful recruitment links: For information relating to the research project please contact the lead Supervisor via: o.m.rodriguez
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conservation managers and incorporation of project findings in BCT’s work on UK bat favourable conservation status. Useful recruitment links: For information relating to the research project please contact
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globally. The direction of the project will be developed jointly by the student and supervisors, including which monsoon regions are of most interest and whether to produce new idealised climate model
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About the Partnership This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists
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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