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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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spectroscopy and HPLC), organic synthesis, electrochemistry, hydrogel soft matter and modelling. You will be mentored by Dr. Maguire and will learn how to design and manage projects, how to conduct research, how
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Supervisory Team: Prof Neil Sandham PhD Supervisor: Neil Sandham Project description: This project is focused on scale-resolving simulations (large-eddy and direct numerical simulation) combined
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Cranfield University and Magdrive will study plume effects of Magdrive's dynamic pulsed plasma thruster on relevant targets. Simulation of plasma expansion and condensation in the space environment will be
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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
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will lead the development of novel motor topologies optimised for this cutting-edge material. Supported by experienced supervisors, the student will be able to design, model, and validate working
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information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high
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intelligence, multi-agent systems, and the design of AI models. They will also acquire transferable skills in interdisciplinary problem-solving and innovation, which will significantly enhance
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discipline. Before you apply, please contact the supervisors with a copy of your CV: Dr Sean Holman and Dr Michael O’Toole: sean.holman@manchester.ac.uk michael.otoole@manchester.ac.uk
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physics background. Experience of experimental and computational modelling of icing physics, instrumentation and imaging techniques would be an advantage. Funding The Centre of Propulsion and Thermal