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Field
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your academic profile in a friendly, supportive, and dynamic centre, with the possibility of contributing to scholarly and policy publications, research seminars and transitioning into a PhD and/or
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 13 000 students, 400 professors and close to 4 500 other faculty and staff working on our dynamic campus in Espoo
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Programme: Hybrid CFD and process simulation for process intensification of post-combustion CO2 capture School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof
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experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University. About the host University/Centre Cranfield is an
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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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includes projects in biomedical instrumentation, experimental hardware automation and programming, and computational science. Scientific publications in a relevant area are considered a significant benefit
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technicians. Applicants should have experience in experimental fluid mechanics and aerodynamics. Prior experience in aeroacoustics is desirable. Applications are welcome from candidates currently pursuing
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
computing facilities. The Centre supports research in digital twins, knowledge-based systems, AI, and immersive technologies such as VR and AR. The candidate will work independently and collaboratively with
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-sensory perceptions, design features and vehicle dynamics. A focus on these factors not only has the potential to improve customer satisfaction but also reduce driver workload, increase usability and
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integrates dynamic “smart” materials into 3D-printed structures, opens new frontiers in both bioelectronics and solar energy harvesting. Our goal is to create adaptive electrode architectures. These advanced