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for success: A PhD awarded in a relevant discipline e.g. social sciences, history, science and technology studies, anthropology, or other discipline with a strong background in conducting qualitative research
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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
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sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The outputs of this work will support practitioners in making informed investment decisions
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of Engineering at the University of Birmingham and delivered in collaboration with industrial partners and the Quantum Technology Hub in Sensing, Imaging and Timing (QuSIT) – one of five national hubs funded by
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immune regulation, inflammation, or disease susceptibility. This project offers a unique opportunity to work at the intersection of human genomics, microbiome science, and computational biology, with
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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of...
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paper production, benefiting both industry and the environment. Most of the work will be performed within the School of Applied Sciences, University of Huddersfield, however, this is an industrially
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
engineering and nanotechnology, and it will require the realization of novel devices for wide range of applications such as photodetectors, optical modulators, optical amplifiers, lasers, biosensors etc. based
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had