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, industrial and grid-level power electronics. The purpose is to improve the electric vehicles and the UKs energy infrastructure as we move into a low carbon economy. A paradigm shift in technology will be
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
on integrated GRMs. The successful candidate will hold a PhD (or be close to obtaining) in Applied Physics, Material Science, Nanotechnology, Chemistry or Electrical Engineering with experience in photonics
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Location: UK Other Applications are invited for a PhD project within the University of Nottingham’s Faculty of Engineering, in the Centre for Additive Manufacturing research group (CfAM
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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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Research Groups at the Faculty of Engineering, which conduct cutting-edge research into electric propulsion systems, composite materials, and advanced simulation technologies. Vision We are seeking a highly
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PhD Project: 3D-printing next-generation electro-actuators for soft robots and devices Applications are invited for a PhD project within the Faculty of Engineering, in the Centre for Additive
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1st or upper 2:1 degree in mechanical engineering, aerospace engineering, materials science, electrical engineering, or a related field. Desirable skills include: Interest or background in composite
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both the fundamental physics of electromagnetic materials and practical applications in 6G communications. The PhD is 4 years and funded by DSTL (Defence Science and Technology Laboratory), and you will
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interdisciplinary team working on the development of cross-platform alignment technologies that integrate material science, process engineering and sustainability analysis to deliver scalable solutions for circular
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. The start date is October 2025. Are you passionate about applying computational science to real-world engineering problems? Do you want to develop digital twins of materials that can predict performance and