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the data to statistically predict match outcome and league position. Initially, these models will be based on legacy data from our project partner, Scarlets Rugby. This dataset comprises individual athlete
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A 4 year PhD studentship in mathematics, funded by the Royal Society. Type of award: Postgraduate Research PhD project: Eigenvalues of large random matrices and statistics of characteristic
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usefulness of the forecast, and perception of forecast performance by the public. Statistical post-processing techniques can help to reduce forecast errors by training machine learning models on data sets
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analysis in biomedical data, in affiliation to the Artificial Intelligence Research Centre . The successful candidate will develop statistical and machine learning techniques to analyse biomedical data. High
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, outcomes, and real-world impact. The student will be supported by a supervisory team including Professors in hepatology and epidemiology, and experts in computational statistics and model fitting using
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occurrence, aetiology, outcomes, and real-world impact. The student will be supported by a supervisory team including Professors in hepatology and epidemiology, and experts in computational statistics and
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control methods. Rigorous theoretical and statistical analysis will be carried out to prove the effectiveness of these proposed techniques. Hence, a strong foundation in mathematical and control theory is
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validating joint models for clinical prediction. Applicants should have: Obtained or working towards a 1st class degree in Mathematics (BSc/MMath) or Distinction level Masters in (Bio)Statistics, Data Science
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subject, such as psychology, biology, human genetics ii) an MSc in a relevant subject involving human genetic research or child development research iii) Prior experience working with statistical packages
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. Eligibility Criteria A 2:1 honours degree, or international equivalent, in a relevant subject (e.g. health sciences, epidemiology, statistics, psychology, medicine, pharmacy, nursing, midwifery, allied health