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UK honours degree or equivalent in Engineering, Chemistry, Applied Physics, or a related discipline; a Master’s degree in these subjects would be of advantage. Funding This is a Fully-funded
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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policymakers. Entry requirements Applicants should hold or expect to achieve an equivalent of a first or second-class UK honours degree in materials science, physics, engineering, or a related discipline. The
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. We are committed to progressing the diversity and inclusion agenda, for example
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to support condition-based predictive maintenance for gas turbine engines. Cranfield has developed unique physics-based technologies on gas turbine performance simulations, diagnostics, prognostics and lifing
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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. Understanding the process of droplet impact and freezing dynamics at high airspeeds, on textured and non-textured surfaces is critical to deciphering the physics behind ice adhesion and accretion. Previous work
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience
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with a background in mechanical, aeronautical, automotive, civil / industrial and/or software engineering (or similar) and/or mathematics and/or physics. The ideal candidate will have a solid background