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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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are invited for a DPhil studentship in Physical and Theoretical Chemistry, available from October 2025, to work under the supervision of Dr Claudia Tait in the Department of Chemistry at the University
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Physics, Theoretical physics, Maths or other closely related subject (e.g. Materials Science with evidence of strong computational background and skills). To apply, please contact the supervisors; Dr Thomas
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for constraining the spin of the compact object being lensed which will involve both theoretical and computational modelling. If you wish to discuss any details of the project informally, please contact: Prof
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complex input. For instance, in physics-informed ML, in addition to data examples used by a standard ML setup, domain knowledge serves as an additional input. It can be in an explicit form of rigorous
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. This project offers a unique opportunity to contribute to both theoretical algorithm design and practical deployment in aerospace systems. Eligibility This studentship is funded through an industrial
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theoretical results with real embedded system demonstrators. A successful outcome of the project is a methodology for distributed control architecture design resulting in resilient aerospace propulsion systems
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to be probed by a combination of theoretical analysis and reverse engineering, in particular for operations that involve inputs and outputs in different formats. Currently, this probing must be done
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PhD Studentship: Microswimmers with Autonomous Propulsion Inspired by Biology and Designed with Physics Are you fascinated by working at the intersection of physics and synthetic biology? This PhD
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and democratic security and to explore the challenges that AI poses to democracy as a form of governance and to the security of electoral and other democratic processes. This will include theoretical