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further including to automated platforms to generate large statistical data sets. We will also experiment with untried higher spatial resolution techniques. The large, multi-dimensional data sets will be
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Klebsiella spp. infections, including those associated with antimicrobial resistance (AMR). As part of this post there is an opportunity to analyse large longitudinally collected sequencing datasets (both
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staff and the adults with learning disabilities that they support. The post requires experience of conducting recruitment and data collection in large studies and previous experience of working with
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will increase to Grade 6. Desirable criteria Experience with skeletal muscle tissue and single muscle cell (myofiber) handling Experience in handling, processing, analysing and interpreting large data
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statistical and computational methods designed to use “big data” and to address questions of direct or indirect relevance to common complex diseases and disorders. The appointee will join the group of Professor
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Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate
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include undertaking research, as appropriate, to support the project including contributing to the delivery and analysis of a large-scale questionnaire survey, conducting qualitative research interviews
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criteria Experience with skeletal muscle tissue and single muscle cell (myofiber) handling Experience in handling, processing, analysing and interpreting large data sets Experience in mentoring, leading and
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, handling and processing of large data sets, knowledge of data acquisition systems, signal and image processing as well as experience in measuring acoustic pressures in cavitating liquids and high-speed
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To undertake research on the evolution of prokaryotic pangenomes using machine learning and AI approaches. The work will involve the analysis of large prokaryotic genome datasets, the development