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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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PhD Studentship: Smart Optical Design:AI Tools for the Next Generation of Cinematic Camera Lenses This project aims to develop a computational framework combining computer vision, computer graphics
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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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engineering, computer science or a closely related field. A solid foundation in computer vision, pattern recognition, AI-related techniques. Hands-on interest in designing methods using foundation models. You
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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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, 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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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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10 Oct 2026 Job Information Organisation/Company SWINBURNE UNIVERSITY OF TECHNOLOGY Research Field Biological sciences Computer science Medical sciences Medical sciences Mathematics Researcher