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The primary objective is the development of computational methods and experimental techniques to investigate failure modes and quantify defects and damage in fibre reinforced hybrid composites used
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tissues or reveal micro- or nano-structural features, like the small air sacs in lungs. To overcome these limitations, alternative X-ray imaging methods have been developed: X-ray phase-contrast and dark
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understanding of gene presence/absence, structural variations, and evolutionary dynamics. In this project we will aim to develop novel dynamic programming computational methods for pangenome assembly of diploid
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the pioneers of methods based on ultrashort laser ablation in liquid ambient. These methods are now considered as the most efficient among laser-ablative pathways to finely control size and physico-chemical
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prior experience in planning and conducting mixed-method research projects, statistical and qualitative data analysis and academic writing are encouraged to apply. Interested applicants are advised
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degraded ecosystems across different habitat types. This is important for establishing the extent to which ecoacoustic methods and metrics are transferrable between places. There is scope within this project
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply