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physics, materials science, computer science or a related discipline background in one or more of the following areas is desired: X-ray or electron imaging techniques, and image processing proficiency in
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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. This studentship addresses this critical gap by leveraging recent advancements in plankton imaging data classifiers’ translatability across multiple instruments’ output. It will apply existing biodiversity policy
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policy frameworks. This studentship addresses this critical gap by leveraging recent advancements in plankton imaging data classifiers’ translatability across multiple instruments’ output. It will apply
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phenomena such as velocity and homogeneity in hierarchically porous Si?based materials conduct multiscale imaging using MHz X?ray projection imaging, X?ray micro- and nano-computed tomography, and scanning
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, using signal processing/machine learning techniques, to realise all-weather perception in autonomous vehicles with high-quality multiple-input-multiple-output (MIMO) radar sensing/imaging. The project
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at the Swammerdam Institute for Life Sciences . This project aims to uncover (1) cellular processes regulated by multiple co-occurring signals, (2) molecular components of signal integration pathways, and (3
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
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learning has strong potential for computer vision, from hyperbolic image segmentation [2] to hyperbolic tree embeddings [3] and hyperbolic vision-language models [4,5]. [1] Nickel, Maximillian, and Douwe
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monitoring. To address these limitations, the proposed research will integrate UAV-based imaging, satellite remote sensing, and AI-supported classification workflows to quantify lichen distribution at multiple