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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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supportive community that meets regularly through a variety of formal and informal events (e.g. fieldtrips, seminars, communities of practice meetings, reading groups). The knowledge, skills and experiences
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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quantitative methods to real-world environmental challenges, we would love to hear from you! Entry requirements Applicants should have a first or second class UK honours degree or equivalent in a related
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project will develop novel methods for modelling and controlling large space structures (LSSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. Working with leading
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models. Partners will contribute amphibian ecology expertise, (semi-)field-method support and links with amphibian networks across the UK and Europe. Cranfield University will host the project within its
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-the-loop testing, and advanced AI methods will further enrich the student’s research experience. The student will have the opportunity to join a vibrant community and team of researchers. This project will
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research opportunity focuses on advancing large-scale additive manufacturing using metal wire as feedstock and electric arc as the heat source. The project aims to develop an innovative and efficient method
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
difficult to detect using conventional techniques. Traditional NDE methods are often slow, manual, and limited in their ability to quantify or localize internal damage accurately. With the growing demand
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with UKAEA, providing opportunities for engagement with leading fusion research facilities. Together, they offer a wealth of experience in integrating simulation and experimental methods to solve real