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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
-latency, and scalable operation in aerial 6G networks. In this regard, Large Language Models (LLMs) have recently emerged as a key technology to achieve adaptive 6G spectrum management. The core idea of LLM
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private. This PhD will focus on three strands of work: 1) Innovate NILM model structures. Design efficient neural network architectures for both aggregator and client models that meet strict accuracy
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prevalent noncommunicable disease globally. The confined and complex architecture of the oral cavity, particularly in regions such as dentinal tubules and root canals, makes effective antimicrobial treatment
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to guide conservation under accelerating climate change. This PhD project will therefore integrate climate data with physiological and behavioural systems, by creating a digital twin framework that
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regions (UL-CDRs) represent a unique structural class with exceptional potential for targeting otherwise inaccessible epitopes. These UL-CDRs feature a distinctive architecture comprising a β-ribbon stalk
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healthcare settings. Approach and Methods: Synthesize gold nanostars with optimised optical and surface properties for enhanced plasmonic signal amplification Engineer LFA architectures incorporating salt
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, visions and concerns in innovation. Entry requirements The standard minimum entry requirement is 2:1 in Geography, Sociology, Political Science, Anthropology, Design, Architecture, Engineering, Computer
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of architecture standards and inform training and development of the systems modellers of the future. Programme and Location: The Engineering Doctorate Researcher will follow the EngD in Model-Based Systems
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This fully-funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA), Cranfield University and Spirent Communications, offers a bursary of £24,000 per annum, covering full
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costs [1]-[5]. Building on these previous findings, this PhD project will design a new MARL architecture that incorporates model uncertainty, cyber-attack scenarios, and network reconfiguration events