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Annual tax-free stipend of £22,780/year for 4 years, full coverage of tuition fees for UK/Home Students, plus training/travel funds Placed On: We invite applications for a fully funded PhD research
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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THR demand in younger patients expected to increase fivefold by 2030, revision surgeries will also rise. To improve implant positioning, image-guided navigation is increasingly used in complex THR
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early ‘prodromal’ stages) is yet to be established in large community settings. This PhD project will examine the effectiveness of AI-based analysis of eye images in predicting cognitive/neurodegenerative
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awareness capabilities. The PhD will involve both simulation and experimental work. This includes designing and testing optical instrumentation and conducting observation campaigns to image and track
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Automation (ICRA), ROSCon and others. This PhD offers extensive transferable skills, including expertise in robotics, navigation, sensors, and system design. Graduates will be well-positioned for careers in
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and biomaterials research within the Lab. This experimentally focused PhD position will fit a wide range of application interests, suitable for applicants from all engineering and scientific backgrounds
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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of GPUs and/or time in either training or inference procedures, which pose considerable challenges to both academia and industry for widespread access and deployment. In particular, the sampling process of
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PhD Studentship: Towards Sustainable Powertrains: Enabling High Motor Performance without Rare Earth Materials and Energy intensive Manufacturing Processes The University of Nottingham This project