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machine learning model to rule out heart attacks in the emergency room, which has the potential to translate to large savings for healthcare systems in the world, (2) a computational modelling to assist in
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to Philip.torr@eng.ox.ac.uk For more information about working
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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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at the Nuffield Department of Population Health (NDPH), the Big Data Institute (BDI), and the Department of Psychiatry. You will Develop, implement, and adapt existing self-supervised and multimodal learning
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-performance or cloud computing environments. Need strong data management and database skills, expertise in clinical phenotyping ontologies and the application of machine-learning/AI methods to biomedical data
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-performance or cloud computing environments. Need strong data management and database skills, expertise in clinical phenotyping ontologies and the application of machine-learning/AI methods to biomedical data
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, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine visual and textual information. By developing
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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their
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a track record of designing neuromodulation and neuroimaging studies in healthy participants, of using computer programs to design experimental paradigms, analyse data and conduct advanced statistical
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* together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is