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Field
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healthcare imaging. This post is focused on AI-assisted ultrasound guidance building on the group’s prior work on video and multi-modality ultrasound analysis. The appointee will be part of the Noble research
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validate new technologies for the diagnosis, prevention, and management of sport injuries, with emphasis on safety rather than performance. You will be responsible for the design, execution and analysis
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on developing advanced new algorithms, testing and validation, and applications in medical neuroimaging and non-imaging modalities. The candidate will contribute to the overall research goals and objectives
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of various psychopathologies and the target engagement by various intervention modalities. Clinical study design, neuroimaging study design, data collection, data analysis, and interpretation of data
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computer vision. Experience with multi-modal data fusion and alignment techniques. Experience with spatial transcriptomics or other -omics data analysis. Proficiency in Python programming and scientific
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and to users Validating predictive models in close collaboration with clinical partners, and interfacing image-based models with other modalities Writing and presenting scientific work at top
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discovery, scientific programming and genetic analysis. We encourage applications also from candidates with little background in biology or medicine, and a keen interest to learn. Recent publications: Clarke
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processes, Bayesian inference, signal models, sampling theory, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis
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deep learning approaches on a wide variety of clinical data modalities, such as structured data, imaging and testing data, and free-text clinical notes. The researcher will have the opportunity to work
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TensorFlow or PyTorch is a strong advantage Experience with VAEs or related frameworks and statistical analysis of high-throughput omics data is an advantage Knowledge of multi-modal data integration, gene