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an expanded M.Phil in Public Policy, and the development of a new PhD programme. It is led by its inaugural Director Professor Michael Kenny, Professor Diane Coyle, the Bennett Professor of Public Policy, and
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across Europe with 16 doctoral students at the interface of artificial intelligence and mass spectrometry proteomics. In collaboration with researchers at the University of Oxford, we seek a candidate to
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
and data capacity of Artificial Intelligence systems, i.e., ‘machine-speed’ operations, without disrupting or compromising such operations? How can we ensure humans retain authority for actions
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of compact robot models. The challenge is building capable robot intelligence with limited real robot data and compute. Interaction data is costly and slow to collect, while deployment requires real-time
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control platforms, advanced microcontrollers, distributed control algorithms, and artificial intelligence techniques, including neural networks and evolutionary optimisation methods, to enable the efficient
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Placed On: Northampton Square, London, EC1V 0HB, UK Closes: 31st August 2026 Applications are invited for a PhD studentship in the Department of Computer Science at City St Georges, University
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, Edgmond, Shropshire, UK. Project We are seeking a highly motivated PhD candidate to join our research team working at the intersection of robotics, artificial intelligence, and agriculture. The project will
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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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actuation, feedback and computation across their soft bodies, blurring the boundary between material and machine. Our work hints that key platforms to capture such material intelligence are ‘robotic materials
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control platforms, advanced microcontrollers, distributed control algorithms, and artificial intelligence techniques, including neural networks and evolutionary optimisation methods, to enable the efficient