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and develop advanced cryogenic power electronics solutions for key net-zero applications such as all-electric aviation and wind energy. This fully-funded PhD project will provide the opportunity
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techniques. We are a community based on mutual support and collaboration. Through our Doctoral College there are continual opportunities for building important research skills and networking among your peers
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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· Research Area: Civil Engineering · select ‘PhD Civil Engineering (full time)’ as the programme of study You will then need to provide the following information in the ‘Further Details’ section
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on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners.
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(coordination) and safety constraints can be intractable. Your work will bridge this gap by providing generalizable, provable design approach that apply across a wide range of networked systems. This 3.5-year PhD
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at Cranfield which has a strong collaborative history with industry in the field of atmospheric icing science research. This programme provides the PhD candidate with an outstanding opportunity to work across a
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may be possible, please contact Professor Kamalan Jeevaratnam once the deadline passes. You will need to meet the minimum entry requirements for our PhD programme . This is an interdisciplinary project
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for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners
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requirements. Build appropriate networks within the team. See full advert for full information. Education, qualifications and experience Essential MSc degree in a relevant discipline to the project: social