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funded studentships for October 2026 entry. The programme offers postgraduates the opportunity to undertake a 4-year PhD research project whilst enhancing professional development and research skills
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
on this foundation, this project will advance distributed and lightweight LLM architectures to enable resource-efficient spectrum allocation across highly dynamic and interference-prone 6G aerial environments. As 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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. This probabilistic approach reflects the uncertainty and variability of geological systems, providing, in addition, a measure of confidence. (4) Calibration Ai architecture. The final component develops a calibration
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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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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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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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question, but which also provide ways for insights from these engagements to feed into innovation processes on an ongoing basis. This PhD project will develop and evaluate an approach for mapping diverse
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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