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the need for sustainability to achieve Net-Zero goals. Cyber-Physical Systems (CPS) integrate machines, robots, and AGVs, but challenges like mechanical wear and electronic errors pose risks to efficiency
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underlying material perception: our ability to determine how materials will feel to touch, just from seeing them. Understanding this process has practical consequences for improving tactile sensing, robotics
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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reusable launchers, autonomous robotics, and advanced materials could redefine how we design space structures. The ability to remotely assemble orbital systems from multiple launcher payloads would allow
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delivery drones, care robots, online health communities, AI-driven tools for communication and story-telling etc.) are being designed to overcome the current care crisis (aging population, shortage of care
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that would give you an advantage) Experience in computational modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning, robotics). Experience in annotation software such as
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sensing, in-cabin monitoring, occupancy sensing, drones, multi-copters, gesture recognition, smart buildings, smart street lighting, smart factories, healthcare, and robotics. The emerging radar sensors in
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failure analysis using advanced finite element models and simulation techniques. This is enabled by digital and sensor technologies such as artificial intelligence, computer vision, drones, and robotics
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Candidates will prepare a doctoral thesis in the field of neuroscience (project duration: four years). Research topics are olfaction, vision and acting, robotics, learning and memory, cortical and synaptic
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applicant will work in a team environment whose mission is to develop robotic systems to interact closely with the natural and built environment while working with end users of these systems. The successful