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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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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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-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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complimentary computational studies to predict the intake aerodynamic characteristics and aid in the experiment design. This position is part of the CDT in Net Zero Aviation, which offers a modular, cohort-based
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities. How to apply
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, intake design, and computational methods. The research will directly support the Net Zero ambitions and contribute to the UK’s strategy of global competitiveness in aero-engine technologies. It will also
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, in collaboration with Rolls-Royce, will develop methods for defining fuel systems suitable for the ultra-efficient engines that will enable net zero aviation by 2050. This project aims to deliver a
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part of the EPSRC Centre for Doctoral Training (CDT) in Net Zero Aviation, offering an integrated, multidisciplinary training programme focused on innovation, collaboration, and inclusive leadership. As
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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by