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a strong background in artificial intelligence and 3D computer vision, with solid programming skills and an interest in 3D data processing (e.g. point clouds and neural scene representations), along
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, computer vision or machine learning or Documented Experience with 2D or 3D biomedical imaging, quantitative or multimodal biological datasets. Familiarity with biomaterials, tissue engineering, scaffolds
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deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations. Encode geological relationships in a knowledge graph that stores
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, and regenerative constructs. The project combines advanced 2D and 3D bioimaging, including micro/nanoCT, confocal microscopy and SEM, with computational image analysis, computer vision and machine
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(such as vision, language, 3D). How to apply If you have any questions, contact the principal supervisor, Dr Adrian Davison . To apply you will need to complete the online application form for a part time
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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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, relating to craniofacial identification research and 3D digital avatars. You will require a 3D animation, CGI, computer science and/or anatomical modelling background. A knowledge of anatomy and 3D
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Developing and validating 3D-OCT-based tools for posterior eye shape characterisation Applications are invited for a fully funded three-year PhD studentship sponsored by Carl Zeiss AG. The
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the deadline. The project topic is flexible and will be developed jointly by the student and the supervisor, depending on the candidate’s background and research interests. Possible directions include 3D body
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imaging (crucial) Experience with image segmentation, deep learning, or computer vision. Experience with 3D image processing or inverse problems. Experience with experimental research and data acquisition