34 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" "Univ" positions at The University of Manchester
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its frontier by integrating mechanistic artificial intelligence with robotic additive manufacturing systems to enable intelligent metal processing. The research will develop physics-informed and data
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Sciences, Materials Science, chemistry, physics or a related physical science discipline to join our research group. Some knowledge or previous experience in petrology, materials processing, coding or data
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric
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formed during late-stage deglaciation and subsequent marine transgression. These data will provide critical constraints for palaeoclimatic reconstructions and help quantify the magnitude and style
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to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric archetypes (e.g. curved vessels, bifurcations, side branches
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studies, the reactivity of MOFs against ammonia and nitrogen oxides will be examined by EPR. This enables information about metal sites, redox transformations, and possible host-guest interactions
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information, please contact main supervisor; Prof Michael Greaney (michael.greaney@manchester.ac.uk ).
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waste)? How much of an improvement to air quality could be achieved by improving user behaviour? The student will use this data along with DEFRA commissioned surveys, together with laboratory data and
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imaging. Novel data workflows will be developed to allow parallel multi-element measurements. The project is interdisciplinary, combining instrument development, analytical chemistry, laser physics and
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knowledge for sustainable formulation design. Using different data-driven feature representations, AI foundation models will generate chemical embeddings to predict key physicochemical properties. Coupled