607 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"IMEC" PhD positions in United Kingdom
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an efficient and mature technology, yet it requires high temperatures and has a large carbon footprint. This PhD project addresses a key challenge: efficiently producing bio-methanol from abundant
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Qualification: Doctor of Philosophy in Engineering (PhD) Eligibility: UK Students, EU Students, International Students Award value: Home fees and tax-free stipend - See advert for details Start date
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online via the above 'Apply' button. Under Campus, please select *Loughborough* and select Programme *Mechanical and Manufacturing Engineering*. Please quote the advertised reference number ‘SF-ST-2026’ in
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Rolls-Royce University Technology Centre (UTC) in manufacturing and On-Wing Technology, The University of Nottingham. Applicants are invited to undertake a three-year PhD programme in partnership
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with our industrial and UK university partners, a novel research programme will be implemented based on modelling, control and optimisation of an integrated power and transportation networks. Key
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. This interdisciplinary PhD is ideal for candidates with a background in chemical engineering, physics, soft matter science, computational chemistry, and/or related disciplines. Early applications are encouraged, as the
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the deadline. The project is to contribute to a major Ministry of Defence (MOD) research programme intended to develop generation after next technologies for applications in defence and security, and this
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be made online . Under programme name, select * Mechanical and Manufacturing Engineering’* Please quote the advertised reference number: * FP-LJ-MTC/26* in your application. Applications must include a
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welcome motivated students with a strong background in engineering or science, such as physics, mathematics, computer science, or related disciplines. Skills in mathematics, computational modelling
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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