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can be leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models
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FluxBEATS that integrates geological observations, cutting-edge geochemical and biogeochemical analyses, data and modeling, from modern volcanic systems along mid-ocean ridges and back-arc spreading centers
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crossing the disciplines of computational mechanics and biomedical engineering? Do you have an interest in developing computational models to investigate the lubrication of natural joints? Do you want
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Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management
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key properties of quantum machine learning models—expressivity, generalization, and adversarial robustness, and the inter-play between them. These foundational insights will guide the design of novel
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, glioblastomas, colon cancer, and lung cancer. Advancing precision oncology through machine-learning models: We integrate multimodal patient data, including multiomic data and health record information, to develop
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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modelling will be useful for evaluating these options. To that end, this team will develop a novel analytical simulation framework and use it to address these pressing questions, in close collaboration with
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time series modelling, age-period-cohort modelling and structural equation modelling. Whilst applicants need not to be familiar with ALL of these, they ought to be familiar with ONE (and to state in
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Science methods and AI models while ensuring their principled and competent adoption across disciplines. Our approach is anchored in fostering an interdisciplinary and inclusive culture in research and