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Field
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Motivation: Soft and flexible materials (e.g. polymers, hydrogels) have revolutionised many areas of science and technology ranging from flexible electronics to soft robotics. Scientists now predict
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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physiology, electronics, robotics, microengineering, materials science, AI, and pharmaceutical science. The doctoral candidates (DCs) will receive comprehensive training through research projects, secondments
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Project descriptionAutonomous systems are intelligent agents—such as robots, vehicles, or drones—that can sense their environment, make decisions, and act independently. When multiple such agents
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, at the University of Cambridge, UK. The Postdoc will work together with a team of students and research collaborators on the development of learning-based discovery of robot task/environment designs
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critical computing. High-level topics include: social identity cues in the design of LLM-based chatbots or social robots trust and reliance on conversational agents designed to be charming and disarming so
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robotic systems and AI models. You will learn how to programme advanced robotic systems and how to implement aspects of deep learning and neural networks for chemical property prediction. You will be part
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the development and application of digital, robotics and sensor-based technology to address key challenges in ageing, which are to enhance cognition, promote independence and foster social connectedness
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Overview: This exciting PhD opportunity is at the intersection of aerospace engineering and cutting-edge technology. It focuses on developing an innovative ground-based robotic inspection system
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of the event-based RL learning rule. Benchmarking criteria include accuracy, latency, data efficiency, and energy consumption to reach a learned solution on small robotic control tasks. Your tasks in detail