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, introduces human error, and creates line-of-sight occlusions, disrupting surgical workflow. This interdisciplinary project aims to overcome these challenges by developing a vision-based marker-less navigation
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Engineering, Computer Science or related disciplines. Experience in autonomous system, manufacturing/robotics and machine vision development will be an advantage. To apply please contact the supervisor, Dr Kun
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combination of academic and industrial challenges which will enhance the student’s ability to tackle complex intellectual and practical aspects of computer vision and robotics. We are seeking talented
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A continual learning approach for robust robotic control in electric batteries assembly. This project is an exciting opportunity to undertake industrially linked research in partnership with
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-effectors and the host robot. Smart sensing systems to support automated manufacturing and maintenance, repair & overhaul. We refer here not only to conventional sensing, e.g. vision, orientation