20 data-"https:" "https:" "https:" "https:" "https:" "https:" "Helmholtz Zentrum Dresden Rossendorf" PhD positions at The University of Manchester in United Kingdom
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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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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
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to national priorities on sustainable agriculture and Net Zero targets. Furthermore, this project will enable the student to acquire skills in data analysis and visualisation, experimental planning, working
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, ion beam transport and detection, laser spectroscopy for atomic and nuclear physics, high voltage systems, and data analysis. The minimum academic entry requirement for a PhD in the Faculty of Science
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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to create a scalable, data-driven framework for adaptive apparel innovation through the integration of emerging digital technologies and engineering methodologies. It will: Investigate how 3D body scanning
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as the advert will be removed once the position has been filled. As AI systems scale, privacy, security, and trustworthiness emerge as challenges. For instance, private data may be leaked during model