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Department/Location: Department of Engineering, Central Cambridge We are seeking a highly motivated Research Assistant/Associate to join EPSRC and industry funded Digital Roads (DR) Prosperity
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capital cost and maintenance costs of offshore wind energy. Development of the concept has been funded by the EU H2020 programme www.xrotor-project.eu , EPSRC, the Royal Academy of Engineering and
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Application deadline: All year round Research theme: Systems and Control How to apply: uom.link/pgr-apply-2425 This 3.5 year PhD project is funded by The School of Engineering and is available
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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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genetics training, is preferred. LMB has a collaborative working culture and state-of-the-art building on the Cambridge Biomedical Campus. We have on-site parking, cycle enclosures and excellent public
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-funded doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott
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research programme, titled Collective Action for Race Equity in Health and Social Care (CARE-HSC), aims to address and dismantle structural factors that perpetuate racial discrimination and harassment within
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dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable though not necessary
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be awarded a master’s degree in any of the following disciplines: Chemical Engineering, Physics, Mechanical Engineering, Energy Engineering, Environmental Engineering, Computer Science, or other
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging