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a mixture of experimental and computation work. A current key focus of the group is development of new multiscale modelling approaches, coupled with data driven modelling techniques, to support
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Machine tool dynamics-based digital twins for real-time monitoring of cutting tool conditions in smart manufacturing School of Electrical and Electronic Engineering PhD Research Project Self Funded
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computational and offers an opportunity for a student to apply the knowledge of particle and astroparticle physics and detector technology in other areas which are linked to key issues of the contemporary world
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diseases, combining biomechanical experiments, micro-Computed Tomography imaging, Digital Volume Correlation, and Nanoindentation. The position is funded as part of the project “ChildBone: A novel digital
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”. This exciting opportunity involves leading the development of advanced data-driven mathematical and computational models to suppress turbulence in pipe flows, contributing to pressing engineering efforts toward
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experience, or keen to learn the research knowledge in power systems, cyber-physical systems, computer science, information and communications technologies, and computing and data platforms. The perspective
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that you reference the application criteria in the application statement when you apply. Essential criteria A PhD in Sociology, Media, Cultural Studies, or other social science fields encompassing digital
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Improving Deep Reinforcement Learning through Interactive Human Feedback School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Bei Peng, Dr Robert Loftin Application
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GIF CDT: A Novel Gas/Liquid contactor for Direct Air Capture and industrial CO2 capture technologies
applications for the following project. This advert will close when a suitable candidate is identified, so early application is encouraged. The CDT boasts an exciting and challenging programme specifically
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Next generation modelling for the pharmaceutical industry School of Chemical, Materials and Biological Engineering PhD Research Project Directly Funded UK Students Prof Rachel Smith, Dr Smitha