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sustainable manufacturing practices, reducing waste and environmental impact. This PhD aims to build a generative AI model and algorithm within the automation system integration for optimising manufacturing
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This is a self-funded PhD position to work with Dr Adnan Syed in the Surface Engineering and Precision Centre. The PhD project will focus studying high temperature corrosion mechanisms in details
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, access, technologies, perceptions and discourses converge in time and space to impact the health and quality of freshwaters. In this project, the PhD student will adopt a critical perspective on the cause
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An exciting PhD studentship opportunity fully-funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership (DTP). This project would suit students with a
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The research in this doctoral opportunity will analytical and numerically model the changes in modal responses of a structure under thermo-mechanical loads. Sub-surface fatigue in mechanical structures affecting their fundamental modes within conventional sensing limits. As per published...
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the Connected Waters Leverhulme Doctoral Programme, which is funding up to 18 PhD studentships to conduct multidisciplinary research on freshwater ecosystems, across two universities, Cranfield and Roehampton
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robot as well as the Mechanical Testing Lab in the School of Aerospace, Transport and Manufacturing. At a glance Application deadline15 May 2024 Award type(s)PhD Start date30 Sep 2024 Duration of award4
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Cranfield University PhD research project proposal, with an aim to provide an interoperability platform using ontologies to enable the exchange of data across platforms at scale. The goal of an ontology is to
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This exciting fully funded PhD is sponsored by EPSRC and Northumbrian Water, with an enhanced stipend of £21,500 per annum (with fees covered). This project will deliver strategies to strengthen and
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This PhD topic relates tool development appropriate to the conceptual design of 6th generation combat aircraft. The research will explore relevant feature modelling and progress to explore