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, or intervention using electronic, mechanical, or smart material systems. This particular PhD studentship, based at the School of Engineering, University of Birmingham, focuses on developing a microfabricated
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
. The research will be computational based, and at this stage is still broad, so we can formulate the optimal plan for the right candidate. We will take an interdisciplinary approach, and you will be able
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53 Faculty of Earth Sciences, Geography and Astronomy Startdate: 01.09.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.08.2029
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Project summary: A PhD student is sought to undertake a project jointly funded by University of Strathclyde and National Manufacturing Institute Scotland project. The project aims at researching a new sustainable femtosecond laser nanomanufacturing process to overcome the challenges of atomic...
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The University of Exeter’s Department of Engineering is inviting applications for a PhD studentship fully-funded by the University of Exeter and National Grid to commence on 01 July 2025 or as soon
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scope to define the project during the first year of study in line with their own research background and interests. Applications are sought from people with either a social science master’s level degree
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motivated applicant to undertake an original and applied programme of research on the development of community-led approaches to mental wellbeing in Wigan as part of Edge Hill University’s Civic Agreement
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Human Behaviour in projects targeting social good. Research at N/LAB focuses on the development and application of innovative computational methods using Big Data, Behavioural Science and Machine Learning
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Zhongping.Que@brunel.ac.uk for an informal discussion about the project. Eligibility Applicants will have or be expected to receive a minimum 2:1 or 1st class degree in Materials Engineering, or a related
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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