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Field
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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applications for monitoring and managing aquatic environments under study, the Mekong river delta and the Forth river system Develop, test and apply algorithms for the processing and analysis of satellite data
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial analysis Strong publication record Experience in women and children’s
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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. The successful applicant will use state of the art inference algorithms to design, use and share the findings of epidemiological models that integrate across large and diverse datasets including capture-mark
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Federated learning (FL) is a privacy-preserving distributed learning paradigm that allows different clients to create a shared AI models without having to share their data. Despite these advantages
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systems on software defined radio (SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide
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in implementing, testing and validating complex minimisation algorithms that can be used for adaptive trials. Application & interview 8 Experience of collaborating on successful research proposals
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental