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demonstratable knowledge in advanced statistical methods (e.g., time series regression, binomial regression, Poisson regression, Cox regressions) and scientific academic writing. Further particulars are included
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-of-the-art computing facilities and collaborate with experienced Co-Is at Royal Holloway’s Computer Science Department and UCL’s Advanced Research Computing Centre as well as Project Partners
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in working with large-scale electronic health records (EHR) data, and ideally with strong analytical skills. The post-holder will be expected to apply various analytical methods using EHR data
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data, like electronic health record data, is advantageous. Knowledge of missing data methods is desirable. Further particulars are included in the job description. The post is full-time, 35 hours per
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methodically, follow set processes and procedures and have a high level of attention to detail and accuracy. A pro-active, flexible approach to your work is vital to deal with the competing demands and
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development including extensions, minor projects, refurbishment and a varied programme of work, including some survey work to establish a rolling fabric maintenance plan. You will be responsible
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London, collecting data using mixed methods, organising a feasibility study in primary dental care practices, analysing data, and disseminating findings. About You Candidates should have a PhD in dental
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NIHR co-funded project to determine appropriate methods of analysis for cluster randomised trials with rare outcomes. The project is motivated by a large cluster randomised trial conducted in Niger
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About the Role The School of Electronic Engineering and Computer Science (EECS) is extending its expertise in the area of electrification of transport and grid systems on top of an already
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. They support excellence in research and teaching through comprehensive services such as high-performance computing, data storage, desktop support, and digital solutions design, ensuring seamless IT experiences