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generic compression tools exist, they often fail to fully exploit the specific redundancies found in 3D tomographic data. You will exploit your signal processing knowledge with statistical mathematical
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Research area and project description: AI data centres are digital engines, yet ~30% of energy is wasted as heat in power conversion and distribution. Directly addressing the UK’s Clean Power 2030
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. Elements such as copper and tin, which are the focus of this project, enrich at grain boundaries during thermo-mechanical processes used to achieve the desired steel microstructure. In this project, you will
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clinicians and industry partners, access real-world healthcare data, and benchmark your decision support tool against an existing UKCA-certified digital diagnostic platform. This PhD is ideal for students
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Sponsor/ Supporting Company: Johnson Matthey Research Group: Advanced Steel Processing Group Eligibility This project is funded via the UKRI IDLA sponsored by Johnson Matthey with the University
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by temperature, moisture, and oxygen, with particular emphasis on hydrolytic and thermo-oxidative ageing processes. Both short-term mechanical properties (e.g. tensile strength and impact resistance
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will pioneer the transition from laboratory-scale demonstrations to scalable, manufacturable acoustic structuring of polymers, representing a step change in composite processing. You will be supported by
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: · Working knowledge of MATLAB/Python and signal processing · Basic understanding of electromagnetics · Experience with CAD and mechanical design How to apply: Interested candidates should contact Dr Hungyen
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robotics, AI, and next-generation networks to make remote surgery safer. Robotic-assisted surgery is becoming increasingly common in modern healthcare, enabling greater precision, smaller incisions, and
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Robotic-assisted surgery is rapidly expanding in the UK, with over 100,000 procedures performed in 2024 alone. Emerging 5G and future 6G networks are making remote surgery increasingly feasible