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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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combines advanced machine learning with medical imaging physics to develop next-generation tools for biomarker extraction and clinical decision support. You will develop innovative generative models using
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About Us The applicant will join the new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will
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innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a complete in silico framework to accelerate the discovery
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are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models using multimodal data including neuroimaging
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to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and predictive analytics Good communication skills and
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statistical and machine learning techniques to analyse how network activity changes in response to these perturbations. This involves identifying patterns, dependencies, and emergent properties across different
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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for analysis of large-scale bulk and single cell data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data