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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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addresses of no less than three and up to five references. Additional documents should be uploaded as one PDF to the “other documents” selection in the application. 4. A personal research program vision
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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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10 Oct 2026 Job Information Organisation/Company SWINBURNE UNIVERSITY OF TECHNOLOGY Research Field Biological sciences Computer science Medical sciences Medical sciences Mathematics Researcher
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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