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Fixed-term: The funds for this post are available for 3.5 years in the first instance. Dive into the future of visual technology with a studentship at the forefront of modern imaging science, where
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computer vision method for medical image analysis principally in oncology or pathology, with a possible focus on generative AI, explainability, multi-modality. Candidate’s profile A good Bachelors degree
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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methodology to generate confidence in such decision, potentially reducing maintenance costs and down-time for offshore wind energy production. The images taken by each drone are loaded into the pre-processing
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, using signal processing/machine learning techniques, to realise all-weather perception in autonomous vehicles with high-quality multiple-input-multiple-output (MIMO) radar sensing/imaging. The project
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power consumption. Many emerging biomedical devices, such as non-invasive and indwelling systems, require reliable operation and continuous feedback on biochemical conditions at the device–tissue
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of computer science and engineering. Research areas incorporate digital signal and image processing, sensor network, Internet of Things, healthcare as application area, multimedia, image and video processing, cyber
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and technology. Desirable (not essential): Research experience in sports medicine and technology (e.g. publication or final year project). Programming skills in MATLAB or Python. Application Procedure
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary