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Closing Date Sunday 17 August 2025 Reference PHD002-25 A funded PhD position is available on a project funded by the Leverhulme Trust. Description below: The density of information content in screen media
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at highest risk of kidney function decline, aiding trial prioritisation What you will learn and why it matters This PhD provides a rare skill set sought after in academia, biotech, and healthcare innovation
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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of qualitative research. Expertise will be gained in how to design and conduct a clinical trial. Following completion of the PhD, the researcher should be in a position to design, set up and conduct independent
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Qualification Type: PhD Location: Nottingham Funding For: UK Students Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780
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of our key research directions relevant for this position. Candidates should have (or be near to completing) a PhD in Computer Science or a related subject, or relevant experience. A high degree of
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background, we welcome applicants from other relevant disciplines, including psychology, sociology, criminology, law, and computer science. This PhD studentship is fully funded for up to 3.5 years with a tax
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A funded PhD position is available on a project funded by the Leverhulme Trust. Description below: The density of information content in screen media – i.e., the light, volume and pitch changes from
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. The successful candidate for this PhD position will be involved in the experimental and theoretical development of a new technology called Surface Nanoscale Axial Photonics (SNAP), which will enable
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders