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or sensor arrays. Experience generating, processing and analysing large material property datasets including correlating between multiple techniques, or developing computational reconstruction techniques
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Engineering, Computer Science or related disciplines. Experience in autonomous system, manufacturing/robotics and machine vision development will be an advantage. To apply please contact the supervisor, Dr Kun
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have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in a materials science subject. English language requirements: Applicants must meet the minimum English language requirements
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gas. This project will first review the various thermal separation processes employed at manufacturing sites across Croda and then determine where alternative technologies can provide a material
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reusable launchers, autonomous robotics, and advanced materials could redefine how we design space structures. The ability to remotely assemble orbital systems from multiple launcher payloads would allow
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quantitative methods, prevention science and intervention studies. The postholder is expected to work collaboratively with other programme members spanning multiple academic and professional practice disciplines
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an original programme of research working with ecologists Dr Ashley Lyons and Dr Anne Oxbrough in collaboration with Dr Steven Ewing at the RSPB Centre for Conservation Science. This PhD is part of the Cumbria
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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