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4-Year PhD Studentship: Deciphering how domain organisation regulates heparan sulphate function Supervisors: Prof Cathy Merry, Prof. Kenton Arkill, Dr Andrew Hook Overview Glycosaminoglycans (GAGs
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Join us for this exciting self-funded PhD studentship on " Development of Sustainable and Cost-Effective Coatings to Mitigate Battery Thermal Runaway Propagation" in collaboration with
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, to industrial catalysis and green energy production. This PhD project offers a rare opportunity to explore hydrogen surface collisions at a fundamental, molecular level to gain unprecedented insight into the role
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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equivalent, in a subject relating to biology/bioinformatics/data sciences Competency in using bioinformatic software packages MRes in a relevant discipline and previous experience in handling transcriptomic
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candidates will have a PhD (or equivalent experience) in Computer Science, electrical engineering, or other related quantitative subject. They will have a strong understanding of computer science (hardware and
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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sits at the cutting edge of materials science and solid mechanics, using real-world microstructure data to simulate thermo-mechanical behaviour at the grain scale. Your work will form a key part of a
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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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biology, chemistry, psychology and social science, facilitating knowledge discovery. The intuitively uninterpretable high-dimensional data and network data become visually scrutable upon being mapped into 2