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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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in health and wellbeing. This practice-based research project will align with the University’s civic mission and its research priority on Arts and Health and will be embedded in the development of a
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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
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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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conditions. The specific aims are: To optimise large-scale production of a high-value carotenoid compound that is naturally released in nanoparticle form by a marine alga. Develop a mechanistic understanding
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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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of differential materials within them. The goal of this project will be to develop responsive 4D-printed biomaterial devices for drug delivery or regenerative medicine. The student will formulate new 3D-printable
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mechanisms, with the current generation having significant drawbacks, including low energy efficiency, high operating voltage or temperature. This project will develop the materials, methods, and designs
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Development of novel processing techniques Modelling techniques that can inform the direction of experimental activity Physical, mechanical and materials characterisation techniques Data-driven approaches