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to deepen our understanding of IAI mechanisms and develop innovative antibacterial biomaterials to improve patient outcomes. Structured around three core scientific pillars-regenerative medicine, biomaterial
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, this interdisciplinary project will focus on developing robust, practical tools to assess and predict recyclate quality. The work will involve thermal analysis (e.g. DSC, TGA), rheology, mechanical testing, and molecular
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, engineering (e.g.: mechanical, electrical, electronic, materials) or a related subject. A relevant master’s degree and/or experience in the use of wearable technology will be an advantage but not essential
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sequencing, bulk and single-cell RNA-seq. These methodologies will be refined and later applied to Welsh genomic resources (SAIL/AWMGS) to identify patient subgroups, uncover disease-driving mechanisms, and
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this advanced manufacturing process will open new opportunities: devices with variable mechanical and chemical properties; fully 3D-printed electronics; and devices with mechanical or electrical responses encoded
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: This position is ideal for candidates with a background in civil engineering, structural engineering and mechanical engineering. Applicants should demonstrate: A strong interest in both experimental and numerical
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading