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, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
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models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
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changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and
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(or equivalent) in Physics, Mathematics, Mechanical or Aerospace Engineering, Computer Science or a related discipline. You should be highly motivated, and would be able to work independently as
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Applications are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in manufacturing engineering. The successful candidate will be based
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Verification Tools: Develop AI algorithms that automate the verification process, ensuring systems meet required safety and performance standards. Health Monitoring Algorithms: Implement AI-based monitoring
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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of electrolysis process in green hydrogen production can be improved therefore ultimately lower LCoH, contributing to net zero by generating zero carbon emissions and reducing reliance on fossil fuels. Cranfield is
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access to an NWSSDTP Research Training Support Grant for eligible research expenses. Application process Applications for this ESRC CASE PhD Studentship should be sent by email to the School of Law and
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programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name