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for gastrointestinal (GI) infections. Current PCR methods are limited by detecting only pre-specified targets. Leveraging shotgun metagenomics and advanced bioinformatics, this project will establish the evidence base
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. Reporting to Peter Visscher. To be considered, you must hold a PhD/DPhil in statistical genetics, quantitative genetics, bioinformatics, computer science, statistics, econometrics or another relevant field
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, and bioinformatics. The project will be based at the Biomedical Research Centre at the University of East Anglia, in close collaboration with the Quadram Institute and Norfolk and Norwich University
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/Biostatistics, Data Science, Life and Social Sciences or Medicine (with substantial component and/or experience in quantitative methods), Applied Mathematics, Computing, Quantitative biology, Bioinformatics
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criteria: Knowledge of the molecular basis of ageing, muscle physiology, or the circadian clock. Experience or strong interest in data science, machine learning, or bioinformatics. Exposure to high
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, particularly MRI, medical physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell
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physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell@nottingham.ac.uk . Applications
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represent the research group at external seminars and conferences. To be considered, you must hold a PhD/DPhil in statistical genetics, quantitative genetics, bioinformatics, computer science, statistics
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PhD Studentship: Subcortical brain development and disorders, funded PhD at the University of Exeter
transcriptomics approaches combined with bioinformatics analyses. The project will focus on the emergence of diverse cell-type at critical points of brain development and identify molecular mechanisms underlying
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chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas