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. This is the context of this PhD project. Overview The PhD student will be testing these hypotheses: • There is a strong effect of the vehicle shape on the wake flow and aerodynamic drag. • There is a direct
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27 Feb 2024 Job Information Organisation/Company Cranfield University Department HR & Development Group Research Field Other Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2
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, if close to submitting PhD) or £37,337 per annum (Research Fellow) About the Role To carry out research involving the integration of drone-based imaging, multispectral data analysis, phenotyping techniques, and
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Manufacturing causes 20% of carbon emission globally and consumes about 54% of global energy generation – this means large scale manufacturers need to understand and address their carbon emission
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CFD-FEA Combined Thermal-Fluid-Mechanical Modelling for Defect Control in Additive Manufacturing PhD
MSc and PhD research, and its rolling technology development programme on large-scale additive manufacturing. This project will have close links to EPSRC research programme of Sustainable Additive
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MSc and PhD research, and its rolling technology development programme on large-scale additive manufacturing. This project will have close links to EPSRC research programme of Sustainable Additive
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depends on a large amount of good-quality data. Unfortunately, the availability of good-quality data is typically limited for high-value critical assets. To remedy this gap, this PhD project will focus
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of operating conditions. The analysis will be undertaken using multi-fidelity tools ranging from reduced order models to high-fidelity computational fluid dynamics (CFD). Applications are invited for a PhD
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This PhD project will investigate the recent field of study of Causal Machine Learning, which aims to modify and augment Machine Learning by using Causal Analysis techniques as a way to solve its
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used to predict armour performance against threats, known and emerging. Overview Initially, the successful candidate will identify the critical data capture steps for accurate prediction of material