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Field
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, covering all cardiac conditions. This makes them unsuitable for identifying rare or complex cases, where annotations are scarce or unreliable. Recently developed unsupervised learning methods allow
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This doctoral research will focus on the development, optimisation, and coordinated deployment of advanced aerial platforms, specifically electric vertical take-off and landing vehicles (eVTOLs) and
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Centre for Experimental Fuel Technologies (CEFT), which aims to develop innovative fuel cell and battery technologies, underpinned by fundamental understanding. The Centre is an exciting venture bringing
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) their undergraduate degree in physics, engineering or chemistry (preferably with first class honours or equivalent) and we expect the PhD candidate to develop the expertise required to lead an experimental research
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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define observable events based on expert knowledge and available evidence. Development of a post-race analysis structure, process and data ‘toolkit’ that can build on historical understanding of race
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that rotations play in gas-surface collisions. Working within a well-supported and experienced research group, the student will join and further develop a unique Magnetic Molecular Interferometer (MMI) apparatus
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on developing latency reversing agents as a first step to eradicate the latent reservoir and animal models are being studied to understand these dynamics. This project proposes a novel pipeline of ideas
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to develop novel, bio-inspired neural networks that flexibly and robustly control locomotion in multi-limbed robots. "Self-organised clocks for reliable spiking computation" (Supervisor: Prof Timothy O'Leary
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respond over time (e.g. changing shape), controlled by the arrangement of differential materials within them. The goal of this project will be to develop responsive 4D-printed biomaterial devices for drug