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Field
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Status: Closed Applications open: 1/07/2024 Applications close: 18/08/2024 View printable version [.pdf] About this scholarship Description/Applicant information Project Overview Cerebral visual impairment, also known as cortical visual impairment (CVI), is a visual impairment caused by damage...
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a digital format. Computer visions techniques have been recently developed for structural health monitoring of civil structures, including vibration displacement measurements, crack detection and
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include insertions of needles, catheters, and micro-robots. For effective medical diagnosis and treatment, the device must reach its intended target. In order to increase the targeting accuracy
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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation
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head-on. We will reinvent generative cooperative vision and semantic compression methods so fleets of intelligent machines can perceive the world robustly, efficiently, and in a trustworthy manner—even
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Motivation: Soft and flexible materials (e.g. polymers, hydrogels) have revolutionised many areas of science and technology ranging from flexible electronics to soft robotics. Scientists now predict
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for medical applications, micro-robotics, and materials science. PhD project description: The field of medical therapeutics has valuable applications for mobile devices for efficient drug delivery, called micro
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world-class laboratory facilities in the CfAM engaging with interdisciplinary team with expertise in 3D printing, bio-printing for medical applications, micro-robotics, and materials science. PhD project
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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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, at the University of Cambridge, UK. The Postdoc will work together with a team of students and research collaborators on the development of learning-based discovery of robot task/environment designs