16 computational-materials-science PhD positions at Cranfield University in United Kingdom
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research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects within WAMC. The student will become part of a diverse and dynamic
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additive manufacturing. This project will be closely aligned with the ATI research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
based within the Manufacturing, Materials and Design theme at the Centre for Digital and Design Engineering (CDDE), which offers access to advanced simulation, visualisation, and high-performance
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, in collaboration with Rolls-Royce, will develop innovative coatings to safely contain hydrogen in critical aerospace materials through experimental and computational modelling work. You’ll join a
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there be interest, there is also the possibility of developing teaching and supervision skills on our MSc Astronautics and Space Engineering programme. Sponsored by EPSRC and Cranfield University, this DLA
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testing and computational modelling. You'll become part of a diverse, multidisciplinary team that prioritises equity, diversity, and inclusion, gaining specialist expertise in hydrogen-material interactions
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disruptive aircraft configurations involves combining advanced engineering practices, including computing power, sensing, AI/ML, and system-level engineering. Comprehensive verification and validation
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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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research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle Analysis
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learning from in-service vehicle fleets and predicting remaining useful life. Applications of artificial intelligence and computer science to battery state estimation. Reduced-authority control of hybrid