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Field
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field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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studies (GWAS), fine-mapping, colocalisation, polygenic risk scoring, and Mendelian Randomisation; and (ii) deep phenotyping of multi-modal cardiovascular imaging (MRI, CT, echocardiography) from large
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and qualifications A PhD degree in computational biology, machine learning, computer science, data science, bioinformatics, or a related discipline Demonstrated machine learning experience in form
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strong technical expertise in deep learning, such as models for image segmentation, classification, multi-modal processing, foundation models, or agentic frameworks. An extensive background in computer
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supervision of Master’s students and PhD researchers. Take part in open science and code sharing. Skills, expertise and qualifications A PhD degree in computational biology, machine learning, computer science
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approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design, probability theory
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, machine learning, and AI applications in radiology. The research area includes innovative work on developing Deep Learning Based Image reconstruction in CT on Photon Counting Detector CT with work in
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their valuable contributions and deep connection to country and pay respect to Elders past and present. Advance internationally recognised Antarctic climate and palaeoclimate research Be part of nationally funded
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deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific