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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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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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management of juvenile fish habitats, the student will be trained in a range of inter-disciplinary skills including coastal fish sampling, scientific diving, digital technologies and computer vision. Would
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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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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of vision systems and AI techniques for perception, decision-making, and control, enabling robots to operate reliably in complex agricultural environments. The research will involve design, experimentation
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Glaucoma is a common neurodegenerative disease which can result in irreversible blindness. While current treatments can slow the progression of vision loss in many patients, as many as 40% will
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will