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1. Project overview Electrochemical sensing plays a central role in next-generation health technologies, enabling real-time monitoring of physiological environments with high sensitivity and low
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A fully funded four-year PhD position is available to work on the project titled “Fault-Tolerant Architectures for Superconducting Qubit Quantum Computers”. This position is a collaborative
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PhD studentship in Trustworthy Multimodal AI under Lightweight and Data-Efficient Architectures Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26
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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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early risk identification. This PhD project aims to design and develop personalised 3D-printed insoles with enhanced offloading performance and integrated sensing capability. The work will explore novel
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monitoring, and autonomous systems. However, most advances rely on large datasets and computationally intensive architectures that are impractical for scenarios constrained by limited data and resources
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printing) is a digital technology and a key enabler for realising next-generation equipment designs that are less energy-intensive and more sustainable. This project will explore a new avenue: fabrication of
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consider the use of ultra-stable photonic and Quantum timing sources. The project covers UK tuition fees and the standard UKRI PhD stipend and it is co-funded by the Quantum Hub in Sensing, Imaging and
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, the construction of the geological model remains a significant challenge. The geological model is key because it constrains the volumes and data used when inferring the mineral resources, which in turn inform