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Field
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computing. Current challenges in quantum technology adoption stem from the lack of standardized benchmarking methods and the inherent difficulty in validating quantum devices beyond classical simulation
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The overall aim of the project is to resolve the quantum trust challenge where the inherently quantum nature of these devices, being beyond classical simulation, complicates their straightforward validation
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simulations, covering a range of typical part geometries and deposition strategies, complemented by experimental validation. • Developing an efficient method for converting partial surface temperature data
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Fixed-term: The funds for this post are available for 12 months A position is available for a Postdoctoral Research Associate (Postdoc) in the forthcoming Aspirational Computing Lab in
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become part of a dynamic academic community, work alongside global experts, and make a meaningful impact on research and society. THE OPPORTUNITY College of Engineering & Computer Science is looking
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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engineering or another relevant field applicable to the measurement technology development. For this position, we are unable to consider significantly different backgrounds, such as biology- and simulation
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- skills – experience: analytical skills, ability to demonstrate good knowledge in system modelling – simulation, (classical or modern) control theories or control applications with evidence Desirable
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
at scale? Digital twins offer a promising foundation, but to truly support engineering decisions, they need to go beyond simulation and begin to interpret and reason about the systems they represent
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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon