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(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT
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slow sand filters. This project suits graduates seeking careers in drinking water technology, sustainable infrastructure, and low carbon process design. Drinking water production is under mounting
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industrially-relevant human-made materials. This project will address key priorities in the microscopy sector by developing workflows that integrate cutting-edge imaging and characterization techniques and
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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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diffusion models usually needs to access a pre-trained model multiple times sequentially to generate high-quality images or videos, which is time-consuming. The training process of diffusion models is also
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The rapid growth of the data economy and the privacy implications accompanying it have motivated a new paradigm shift towards decentralisation of data on the Web, which aims to foster data
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Tomography (XCT), a non-destructive imaging technique, to perform crushing experiments of TRISO particles over a range of temperatures, thereby achieving a better understanding of the deformation behaviour
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defects without compromising structural integrity, thus ensuring passenger safety and operational efficiency. The project aims to design and prototype a ground-based automated inspection system capable
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adaptive signal processing whose combined performance and resilience can easily exceed that of the sum of their parts. However, fundamental and significant questions to provide their practical feasibility
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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm