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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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We are seeking an ambitious and highly motivated Research Fellow in Computer Vision to contribute to an EPSRC-funded project, LungSight, focused on visual and acoustic screening for early detection
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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if the candidate can self-learn the knowledge within a short time (e.g., 1 month). Have a degree in computer science, computer engineering, electrical engineering or equivalent. Possessing a Master’s or PhD degree
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to the broader Innovation and Knowledge Centre mission to help bridge the gaps between academic research and industrial impact in this field. A strong background in machine learning, computer engineering, applied
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for 2030+. About you Applicants should have a good honors PhD degree in wireless communications or AI and computer vision fields and a strong track record of publications. Preference will be given to those
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for 2030+. About you Applicants should have a good honors PhD degree in wireless communications or AI and computer vision fields and a strong track record of publications. Preference will be given to those
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background in modelling, interested in thinking about the sea and the plankton which live there. There are dedicated funds for computer equipment, and travel to support you in your scientific work. If you are
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or computer vision with a video focus, and ideally have prior experience of working with echocardiography or fetal echocardiography or related experience on real-world video analysis Informal enquiries may be
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publications in medical image analysis or computer vision video analysis. Knowledge of ultrasound imaging is not a requirement but an interest in research at the interface of machine learning with real-world