68 web-developer-"https:" "https:" "https:" PhD positions at University of Nottingham
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to develop a comprehensive modelling and analysis approach for hydrogen systems, such as electrolysers and their BoP systems, in meeting some of the below challenges in: advanced diagnostics and prognostics
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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engineering, structural, aerodynamic & manufacturing process modelling and optimisation techniques that will transform current design & development practices. Together we will make technological advances
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opportunity is based within the Resilience Engineering Research Group at Faculty of Engineering which conducts cutting edge research into developing modelling techniques to predict ways of improving the design
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sustainable chemical and biocatalytic process development. Vision We are seeking a motivated PhD student to develop a strong experimental understanding of enzyme selectivity when processing mixed biomass
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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Turbulence detection in blood flow using 4D MRI Flow disturbances in blood flow are vital sign of cardiovascular diseases, suggesting a development of turbulent flow due to abnormal heart movement
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collaboration with BP. Vision We are seeking PhD student that is motivated to develop practical rapid analysis tools for BPs biomass liquefaction processes. We are looking for candidates with an interest in
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relying on incremental optimisation of existing materials. By developing novel multilayer dielectric materials with ultra-high breakdown strength, the research will revolutionise electrified technologies
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one of the world’s leading centres for additive manufacturing research and development, invites applications for a fully funded PhD programme. Metal additive manufacturing is transforming how complex