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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine
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an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement
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propellant space propulsion systems. A significant limiting factor of hybrid propulsion systems is the continuous change in surface area of the propellant grain during the combustion process. This changing O/F
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. dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield. His research interests include remote sensing image processing, computer vision and machine learning
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collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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Modern cyber-physical systems (CPS), such as UAVs, next-generation fighter aircraft, and command-and-control (C2) platforms, integrate digital computation with physical processes to make mission
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honours degree in materials science, physics, engineering, or a related discipline. The ideal candidate will be self-motivated, with an interest in both computational modelling and practical manufacturing
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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