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Field
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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. Background and aims: As we move towards the future of large communication networks and remote sensing, applications such as 6G communications will require higher data rates, wider bandwidth, and stronger
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the need for sustainability to achieve Net-Zero goals. Cyber-Physical Systems (CPS) integrate machines, robots, and AGVs, but challenges like mechanical wear and electronic errors pose risks to efficiency
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are part of the programme. The research is funded by the Centre of Propulsion and Thermal Engineering at Cranfield University. The work will be conducted at the Cranfield icing wind tunnel (IWT) based
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One fully funded, full-time PhD position to work with Prof. Mahesh Marina in the Networked Systems Research Group at the School of Informatics, University of Edinburgh. The broad aim
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Title’ using the programme code: 8856F Leave the 'Research Area' field blank Select ‘PhD in Process Industries; Net Zero (PINZ’) as the programme of study You will then need to provide the following
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benefit from an enhanced stipend of £25,726 per annum, undertake an international placement, and complete a bespoke training programme within a cohort of up to 15 students. Students will benefit from being
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emissions from transport. Decarbonising aviation is a vital part of achieving net zero. Hybrid and ‘all electric’ aircraft technologies offer a pathway to net zero. The electrification of aircraft, for both
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the above ‘Apply’ button. Under ‘campus’ please select *Loughborough* and select the programme ‘CDT Engineering Hydrogen Net Zero’. Please quote the advertised reference number *LU-EnerHy-2025-1* under
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significantly reduce carbon emissions and bolster global efforts to achieve net-zero targets. Despite considerable advancement in their operational performance, their life cycle impacts, including raw material