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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms
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Discover your career The world of the University of Nottingham is defined by our people and the values we share. Our environment is an ambitious vision brought to life across vibrant and forward-thinking global campuses. An ever changing world where open minds and diverse cultures are able to...
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. The deadline for a formal application is 5th May. Start date: 1st Oct 2025. Annual tax-free stipend based on the UKRI rate (currently £20,780) plus fully-funded PhD tuition fees for the 3.5 years. Supervisors
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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Digital-Twin Technology to Accelerate Development of Electric Propulsion Systems This exciting opportunity is based within the Power Electronics, Machine and Control Research Institute at Faculty
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REACTOR DESIGNS FOR THE SUSTAINABLE PRODUCTION OF LIGNIN-DERIVED END-PRODUCTS VIA DEPOLYMERISATION REACTIONS This exciting opportunity is based within the Advanced Materials Research Group
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PhD Studentship: Carbon Nanotube (CNT) Winding Development for Electric Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers an exciting opportunity
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propulsion systems. The student will receive full training and support in both modelling and experiments. The skills developed in this PhD are highly transferable and will offer career pathways across sectors
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PhD Studentship Aircraft Electrical Power System Stability This exciting opportunity is based within the Power Electronics and Machines Centre (PEMC) Research Group at Faculty of Engineering which
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and