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Are you a computer scientist or machine-learning researcher interested in making medical-imaging AI work reliably beyond the dataset on which it was developed? We have an exciting two-year Grade 7
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Duration: 1-year appointment with possibility of renewal for a 2nd year. About the Department Our research group works at the intersection of machine learning, medical imaging, biomedical engineering, and
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or experience in digital image processing, computer vision, machine learning, data science, or a related field. Familiarity with Python libraries such as NumPy, OpenCV, Matplotlib, or similar scientific computing
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. Working with images of rare books and manuscripts, maps and modern archival material, you will audit and assess images and their technical metadata, undertake any necessary image renaming or processing, and
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changes required for heterogeneous CAPTURE-PH imaging. Develop and optimise supervised deep-learning methods for segmentation, feature extraction, classification and prediction, including retraining or fine
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, Machine Learning, Data Science, Computer Science, Electrical Engineering, Biomedical Engineering, or a related field. Experience with image analysis, computer programming using Python and R, and machine
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. — Excellent communication skills and a user-centred approach. The following will be particularly valued — Experience in analysis using machine learning or artificial intelligence applied to biomedical images
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Performance Computing Clusters. Preferred Qualifications Experience in Machine Learning analysis of complex image data sets. Experience with spaceborne and ground-based astronomical imaging data sets and
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. Evaluate MR scans for technical quality, collate processed images and sequence of acquisition and label appropriately. Schedule patients for procedures; assist with clerical functions. Charge MR procedures
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-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF, μXAS) and statistical analysis (clustering, machine learning) is preferred. Familiarity with fossilization processes and taphonomic