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by extraordinary ideas - and the people who discover them The Opportunity As a Lecturer (Level B) or Senior Lecturer (Level C) in Radiography, you will help shape the next generation of medical imaging
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testing and assessment of device-assisted aerosol delivery under clinically relevant operating conditions. Data processing, image analysis, uncertainty assessment and experimental interpretation using
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, computer vision, federated learning, foundation models, adaptation techniques, multimodal learning, longitudinal image analysis or related areas, evidenced through coursework, research projects
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-field imaging of dynamic processes" "Multi-scale X-ray speckle-based imaging" "Spectral X-ray speckle-based imaging" "Single-shot multi-projection X-ray phase-contrast imaging" "X-ray virtual histology
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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This project aims to develop a computer vision system capable of detecting and classifying domestic geographic landmarks in images and video content. By categorizing locations such as “childcare
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-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
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The project involves building and curating a comprehensive food image dataset suitable for mobile AI applications. High-accuracy deep learning models will be trained on this dataset and then
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Manager will proactively oversee the operations of the lab, ensuring that strategic goals are translated into effective, coordinated action. The successful candidate will balance big‑picture thinking with
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: [email protected].