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potential later start dates.) Supervisors: Professor Gwyneth Davies, Professor Rich Fry (Swansea University) and Professor Jenni Quint (Imperial College, London) Aligned programme of study: PhD in Population
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Deadline: 31 August 2025 A fully funded four-year PhD position is available to work on the project titled “Real-world quantum verification and benchmarking of noisy hardware”. This position is a
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‘Research Area' field blank select ‘PhD Energy Materials' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: a ‘Personal Statement’ (this is a
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the PhD student in high-performance computing, computer programming, applied mathematics, fluid mechanics, mathematical modelling and data analysis for large datasets -of the order of 100 Terabytes
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both the fundamental physics of electromagnetic materials and practical applications in 6G communications. The PhD is 4 years and funded by DSTL (Defence Science and Technology Laboratory), and you will
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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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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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to the launch of the Bloomberg Cambridge University Corporate Bond Index later in 2025 and the delivery of the ongoing research programme related to the index project. The successful candidate will undertake desk
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, process stability, and the downstream consolidation and performance of remanufactured composites. This fully-funded PhD project fits within a wider research programme with industrial partners and an
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing