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PhD Position – Development of metal-organic frameworks-glass composites for sustainable applications
to staff position within a Research Infrastructure? No Offer Description This is a PhD project focused on the design, synthesis, and advanced structural characterisation of MOF-glass composites
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supervisor, and copy the link to this scholarship website into question 2 of the financial details section. About the scholarship This project aims to mitigate the impact of vehicular collisions on roadside
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details Supervisors: Dr Oksana Trushkevych and Prof Tony McNally Research area and project description: Develop scalable acoustic methods to structure advanced polymer composites for lightweight, low‑carbon
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reinforcement of laminated FRPs using prestressed Z-pins. Your research will contribute to the fundamental understanding and quantitative assessment of whether, and to what extent, SMA Z-pins are an efficient
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simulation, composites manufacturing and advanced sensing techniques. The project will provide opportunities to develop skills in these areas and contribute to the development of the next generation composite
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from E. coli using molecular simulations to understand electron transfer between the second heme b and heme d [4]. To this end, we performed molecular dynamics simulations of the protein within a
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groundbreaking research in sustainable natural fiber composites as part of the TailorFib project, funded by the Luxembourg National Research Fund (FNR) and Austrian Science Fund (FWF). You will be mainly in charge
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You. Choose Duke. Be Bold. Position Summary: The Duke Department of Neurosurgery is seeking a highly motivated Postdoctoral Associate to join our research team investigating the neural mechanisms
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this change and explore cutting-edge methods in chemical, mechanical, and plasma processing of metal ores for a truly circular economy. Correlating experimental, ab initio, and multi-scale simulations, as
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these more abundant metals. However, millions of possible alloy compositions can be formed from just five elements, making empirical composition optimisation impossibly slow. This project will tackle