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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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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
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, understanding how bias propagates through chains of autonomous decisions becomes essential. The candidate will contribute to the development of empirically validated methods for identifying, measuring, and
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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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and approximate costing of the field work, and a statement of the funds available and how the grant funds will be used. A brief reference from your supervisor. Applications to be submitted via email to