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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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analyse large datasets such as the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics to identify activity related to the treatment of community acquired pneumonia. This will require
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multivariable statistical methods. Support for skills development is provided within the Horse Microbiome Research Group and the university’s Doctoral College . Delivery of this project in collaboration with
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. Experience in working with large data sets, knowledge of statistics, and some programming expertise is essential. The project is based in ECEHH, at the University of Exeter’s Penryn Campus in Cornwall, and may
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, psychology, health economics, statistics, health services research), and with a commitment to conducting excellent and innovative research that will advance care, support and outcomes. Example topic areas
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appropriate statistical testing to test the efficacy of the models. The project forms part of a wider effort for developing an effective VR HRI toolkit for improving interactive fluidity between robots and
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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, unit reliability analysis, and shared variance component analysis (SVCA) Create comprehensive data visualisations and perform statistical analyses to assess stability and plasticity of multisensory
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, statistics, sleep medicine, and artificial intelligence, for example. A very significant and specific benefit we can offer is the option for to complete a PhD in the 36-months, if you wish . The opportunity
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teach you many translatable skills and knowledge from the fields of sleep medicine, sleep physiology, statistics, artificial intelligence, and psychology for example. A very significant and specific