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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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Knowledge of medical imaging Good knowledge of image processing Good knowledge of signal processing Good knowledge of AI for medical imaging Ability to use computer programming tools Ability to use word
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research group in computer vision and machine learning, with seminal results in 3D reconstruction from images, scene understanding, deep learning, optimization, sparsity, etc. IMAGINE is part of
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] Cross, E. J., Gibson, S. J., Jones, M. R., Pitchforth, D. J., Zhang, S., & Rogers, T. J. (2021). Physics-informed machine learning for structural health monitoring. Structural health monitoring based
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according to their background and interests. Applicants should have strong quantitative and computational skills (e.g., text or image analysis, natural language processing, large-scale data methods) and a
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and image processing. Prior experience with data fusion, machine learning, or super-resolution methods will be considered an asset. Candidates should be motivated to conduct independent scientific
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parameters to identify regimes that ensure both flame stability and low pollutant emissions. Machine learning techniques have recently shown promise for Design of Experiments (DoE) and interpretation of large