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Field
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sensors to understand their local surroundings at sea and inform optimal action. To ensure safety requires the ability to reliably detect, image and recognise their environment, in terms of surrounding sea
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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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challenges. We choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of Innovation and of Entrepreneurship. We are committed to a differentiated
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of image recognition tools, statistical analyses and the mechanistic description of carbon substrate conversion and growth on solid material. The tasks also include process-related life cycle analyses
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. Collaborate with people with diabetes, diabetologists, software engineers, and clinical psychologists to develop and validate prototype tools. Contribute to qualitative and mixed-methods research to ensure user
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engineering Construction informatics, and in part related to Building Information Modelling (BIM), ontologies, process modeling, databases, open data standards such as Industry Foundation Classes (IFC), and
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Sintorn, Professor in digital image processing, at the Department of Information Technology and conducted alongside researchers developing computational methods with a particular focus on deep learning and
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Qualification type: PhD Location: University of Hull Funding for: UK students Funding amount: £20,780 Hours: Full time Closes: 9 May 2025 Subject areas Engineering & Technology Computer Sciences Mathematics
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, machine learning, and image processing techniques Strong candidates with electrical, mechanical, and biomedical engineering backgrounds can also apply. To be eligible for this scholarship, you must: Meet
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the following skills and qualifications (tailored to the specific project): Driven individuals who want to be a part of a world class team Some familiarity in healthcare or engineering/image based analysis