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Field
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subwavelength features that enable tailored optical responses not found in nature—such as negative refractive index. This funded PhD studentship will develop 3D-printed optical metamaterials to sculpt ultrashort
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Evaluating the use of longitudinal over-the-counter healthcare sales data as a source of syndromic surveillance for community-based infections UK Health Security Agency and Nottingham University
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This PhD focuses on the development of a novel approach to measuring the traction behaviour of artificial turf, supported by FieldTurf, a global leader in the production of artificial turf for sport
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
the crystal structure of the LMTK3 kinase domain and developed the first-in-class small-molecule inhibitor, C28. Despite these significant advances, a comprehensive understanding of the full-length LMTK3
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The project will work alongside Vishay Intertechnology, which brings together a number of world leaders in power semiconductors and energy conversion to develop solutions for automotive, aerospace
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power generation companies—to develop robust methodologies for investigating failure mechanisms in CFRP sleeves. Your contribution will help prevent catastrophic magnet detachment and significantly
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development could be foreseen in Year 4. The recruited PhD candidate will become a member of an active research group working on the development and application of cutting-edge computational techniques
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The project: We invite applications for a fully funded PhD studentship in the Solid Mechanics Group at the University of Bristol to work on the predictive modeling of hydrogen-induced damage in
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, which are prone to failure under extreme rotational stresses. This project responds to industrial demand—highlighted by Aerospace and power generation companies—to develop robust methodologies
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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