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Field
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Programme Overview This PhD is part of the Engineering Hydrogen NetZero (EnerHy) Centre for Doctoral Training (CDT), a newly established EPSRC-funded initiative focused on advancing research and
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
? This PhD, working with Prof. Marcelle McManus , provides an opportunity to work to explore and advance novel decarbonisation solutions for high-energy use industrial sites. Your project will be co-created
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will be around 6th Oct 2025 Project Overview An enthusiastic researcher is invited to join a team to work on the EPSRC Industrial Doctoral Landscape Award project in collaboration with industrial
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adaptive signal processing whose combined performance and resilience can easily exceed that of the sum of their parts. However, fundamental and significant questions to provide their practical feasibility
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conditions. SCR is part of the SLB technology and energy innovation organisation, employing approximately 98,000 people representing 160 nationalities and working in more than 120 countries. SLB strongly
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
at scale? Digital twins offer a promising foundation, but to truly support engineering decisions, they need to go beyond simulation and begin to interpret and reason about the systems they represent
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PhD programme within QMUL’s Wolfson Institute of Public Health, under the supervision of Dr Giuliano Russo (WIPH) and Prof. Pietro Panzarasa (School of Business and Management). As part of the project
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Futures, Faculty of Environment, Science and Economy at the Penryn Campus in Cornwall. This project will use a mixed methods approach to assess the short- and long-term activities, outputs, outcomes and
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the MET office. The student could choose to develop these connections to understand resident experience and behaviours, identify the current barriers and facilitators, to inform adaption measures
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical