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Field
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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performance (10–50 Hz) on hardware with constrained memory, power and compute. The project will investigate training, adapting and deploying compact Physical AI models efficiently. Work may cover vision
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, Computer Science, and Engineering, who wish to pursue their research within one of our research groups. In addition to participating in world-leading vision research, candidates benefit from a comprehensive training
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consider task success, generalisation, reliability and computational efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will
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understanding with language-based reasoning. Process micro-facial expression data more efficiently in computer vision and vision language models. Create a language-guided representation for subtle facial motion
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. Depending on the candidate's background and research direction, approaches may include computer vision, representation learning, graph neural networks, multimodal learning and scientific machine learning. A
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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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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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scholarship for this research and enter “PhD Scholarship in Communication and Navigation for People who are Blind or have Low Vision” as the scholarship name under Research Program Funding of the application
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integrating AI with vision at the edge. Despite recent advancements, the synergy between AI and computer vision remains constrained by fundamental imaging bottlenecks. Conventional HDR techniques frequently