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Field
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applications where robots must interact delicately with the physical world. Current robotic systems rely primarily on vision and force sensors, missing the rich tactile information that enables human dexterity
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integration. Lead and contribute to research involving AI-powered and AI-enabled robotic systems, including deep reinforcement learning, computer vision, and human-robot interaction. Facilitate strategic
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Full time, 8 month fixed term position. Located in the Rose Street Building (J04) on the Camperdown Campus Work with world-leading researchers in robotic sensing and perception Contribute
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on safety, cooperation, and efficiency in human-robot teams. Multi-Modal ML: Expertise in working with diverse data types, such as vision, speech, images, and physiological signals. Experience integrating
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, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and computational
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of design, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and
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of robots to interact with the surrounding environment and humans during the execution of specific tasks. Project description The vision of RAI is aiming in closing the gap from theory to real life, while
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activities. The RAI team is conducting fundamental research in all the aspects of robotics with a specific focus on Aerial and Space robotics. The vision of RAI is aiming in closing the gap from theory to real
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Vision, Robotics, Evolutionary Computation, Deep Reinforcement Learning, and Machine Learning. This should include a proven publication track record. You should also have: Research Associate: A PhD (or
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vision and ultrasound. The goal is to reconstruct boney anatomy (e.g. knees and hips) using robotic mapping techniques. Research will be carried out as part of a new collaborative project involving