21 assistant-and-professor-and-computer-and-science-and-data PhD positions at The University of Manchester in Uk
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scattering with computer modelling such as molecular dynamics simulations and AI-assisted data mining. The new technical capabilities will help bridge the current gap in biocide development, i.e., to link
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computations possible [1]. However, proven scientific applications for quantum computing remain mostly limited to quantum chemistry, materials, and particle physics. Since CFD is one of the most demanding use
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distinct imaging methods to yield a novel imaging method that combines the benefits of both. The aim of this project will be to develop a novel method for fusing the data obtained by x-ray imaging and MIS
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implementing engineering wake models in WRF or similar activities. Production data from simulations will be compared with grid data for validation. She/he will closely work with industry and policy makers
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energy? This PhD offers a rewarding chance to become a materials science expert, combining cutting-edge characterisation with real-world industrial impact. At the University of Manchester, working in
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Alex Leung (Mechanical Engineering at UCL) will also collaborate. he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect
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a tug-of-war. The activation can be performed in solution, with the help of ultrasounds, or in the solid state, by simple stretching. Mechanical bonds have always fascinated chemists because
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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
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Application deadline: 06/01/2026 Research theme: Biological Chemistry This 4-year PhD studentship is open to Home (UK) applicants. The successful candidate will receive an annual tax-free stipend
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stresses. Based on the experimental data, a semi-empirical model to be developed to assess insulation degradation and identify failure signatures that can inform future predictive asset management strategies