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Field
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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disease patients using radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical
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requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in
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internationally recognised for its research in craniofacial biomechanics. Located in UCL Mechanical Engineering and supported by state-of-the-art imaging and material characterisation facilities, the lab focuses
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-quality papers in top journals and conferences. The appointees will be required to: (a) develop novel AI and XR algorithms/software for medical image analysis and computer-assisted surgery tasks; (b
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: Preferably PhD degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written
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thermal imaging data, and potential clinical and signal data, to create algorithms capable of recognizing key clinical activities and interventions. Building on recent advances in computer vision and
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School of Molecular Sciences ARC Centre of Excellence in Plants for Space Integrate advanced cell imaging and synthetic biology to create and characterise the first fully artificial plant chromosome
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School of Molecular Sciences ARC Centre of Excellence in Plants for Space Integrate advanced cell imaging and synthetic biology to create and characterise the first fully artificial plant chromosome