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have a 1st class degree (BEng or MEng/MSc) in electrical/mechanical engineering. Expertise in numerical tools (Ansys, JMAG, .etc) and programming are desirable. Experience in electrical machine
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available from October 2025 under the supervision of Prof. Helfrid Hochegger and Dr. Tony Oliver, at the Genome Damage and Stability Centre, School of Life Sciences. Deadline: 06th of June 2025 PROJECT TITLE
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 28 days ago
? This PhD, working with Prof. Marcelle McManus , provides an opportunity to work to explore and advance novel decarbonisation solutions for high-energy use industrial sites. Your project will be co-created
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. The post is funded by Porticus. The successful candidate will undertake a PhD programme related closely to the work being carried out in the Conversations, Culture and Communion project based at the Centre
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Planning, Henley Business School Supervisors: Dr Emma Street and Prof Gavin Parker Project Overview: Using a governance lens and participatory methods, this project involves local stakeholders and
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happy to discuss this opportunity via email s.calnan@lboro.ac.uk . Supervisors: Name of primary supervisor: Prof Sonya Calnan Name(s) of secondary supervisor: Dr Sarah Dunnett Entry requirements: Subject
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4 year PhD Studentship: Deciphering how domain organisation regulates heparan sulphate function University of Nottingham, School of Medicine Supervisors: Prof Cathy Merry, Prof. Kenton Arkill, Dr
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Applications are sought for a fully-funded 42 month PhD studentship to work with Dr Rachel Nicks and Prof Stephen Coombes on the project: White Matter Computation: Utilising axonal delays to sculpt
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One fully funded, full-time PhD position to work with Prof. Mahesh Marina in the Networked Systems Research Group at the School of Informatics, University of Edinburgh. The broad aim
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning