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Field
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students
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requirements Open to any UK or international candidates. Starting in January 2026. You will need to meet the minimum entry requirements for our PhD programme . By the start of the PhD programme, applicants must
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, building resilience and long-term sustainability. This fully funded PhD includes an enhanced stipend of £25,726 per year, undertaking an international placement, and completing a bespoke training programme
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Engineering, Faculty of Engineering and Applied Sciences, Cranfield University, in the area of performance simulation, analysis, and optimization of supercritical CO2 power generation systems. Cranfield has
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the International and the Home tuition fee for the duration of the PhD/Professional Doctorate programme. Successful candidates will be charged fees aligned to the UK rate per year. The scholarship will be offset
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
relevant field such as engineering, computer science, or applied mathematics. Experience or interest in AI, machine learning, or digital systems is beneficial. We welcome candidates from diverse backgrounds
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applications should be made online . Under ‘Campus’, please select ‘Loughborough’ and select ‘Programme’ as ‘Chemical Engineering’. Please quote the advertised reference number ‘CG-BB-2507’ in your application
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will be jointly supervised by: Dr Dominik Leichtle, School of Informatics, University of Edinburgh Dr Elham Kashefi, School of Informatics, University of Edinburgh Dr Theodoros Kapourniotis, National
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those