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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting-edge deep generative probabilistic models
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the Oxford–Novartis Collaboration for AI in Medicine. You must hold a PhD/DPhil in Statistics, Statistical Machine Learning, Deep Generative Modelling, or a closely related field, together with relevant
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machine learning models (e.g., VAEs, GANs, transformer-based models) to large-scale biomedical and biological data, including developing and optimizing models to predict disease progression and create
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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functions, predictive models or quantum chemistry Machine learning or AI frameworks applied to molecular discovery. Familiarity with cloud or high-performance computing environments. Experience collaborating
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-time academic or research career. The individual will work primarily on the Duke Predictive Model of Adolescent Mental Health (Duke-PMA) study, a multi-site NIH-funded project that leverages artificial
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None Additional Preferred Experience working in one or more of the following areas: Longitudinal data analysis Predictive modeling/machine learning models Biostatistics / epidemiological modeling
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the strategy to be applied for training a learning model to predict wind and precipitation conditions based on a dataset combining numerical weather prediction models and in situ observations; Collect
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other data scientists to advance precision medicine research. Your main tasks and responsibilities: Designing and leading analyses that apply state-of-the-art generative machine learning models (e.g
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gene gain/loss events, horizontal gene transfer, and functional diversification within gene families. You will apply statistical models and machine learning algorithms to identify associations between