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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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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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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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. The Prob_AI hub will focus on probabilistic AI, and bring researchers with skills across areas such as Bayesian and Computational Statistics, Dynamical Systems, Numerical Analysis, PDES, Probability, Stochastic
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)) models are used at all stages of pre-clinical and clinical development, but they are based on mathematical and statistical principles dating from the 1970s. Developing these pharmacometric models remains a
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exposure methods to enhance comprehension of material corrosion in hypersaline environments. Reliable test methodologies and statistical analysis techniques will be employed to assure conclusive
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affect these trajectories using discontinuous growth modelling and/or other appropriate statistical analyses. Additionally, the student will conduct a qualitative interview study to understand
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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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legibility will be evaluated against human response times of user confirmations in a variety of scenarios, using appropriate statistical testing to test the efficacy of the models. The project forms part of a
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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