580 assistant-professor-computer-science-data-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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Funded PhD Studentship in applied geospatial ecology. This project will investigate how dryland vegetation productivity varies across space and time and how it responds to land management. Working with NEOM, one of the world’s largest ecological restoration programmes, the project will develop...
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collaboration across disciplines, for example, public health, business, education, engineering, and governance—to address issues such as health equity, urban resilience, and social innovation. Challenges include
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Doctoral Training Grant Funding Information This funding model includes a 48-month funded PhD Studentship, set in-line with UK Research & Innovation values. For 2026/7, this will be £21,805 per year
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MEng degree in Electrical and Electronics Engineering or Aerospace Engineering. To apply or for further information, please contact Dr Sharmila Sumsurooah Sharmila.Sumsurooah@nottingham.ac.uk Funding
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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information. Starting in October 2026. Later start dates may be possible, please contact Dr Isabella Guido once deadline passes. You will need to meet the minimum entry requirements for our PhD programme. We
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strengths and interests (e.g. geospatial data science or socio-environmental modelling). Funding Sponsored by the Leverhulme Trust and Cranfield University, this Connected Waters Leverhulme Doctoral programme
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: 8060F select ‘PhD in Electrical and Electronic Engineering (full time) - as the programme of study You will then need to provide the following information in the ‘Further Details’ section: a ‘Personal
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workflows and contribute to building UK capability in an important advanced reactor area. The ideal candidate will enjoy computational modelling and quantitative problem‑solving, with a strong foundation in
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First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics, Mathematics, or a related discipline, and demonstrate