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Science, Statistics, Physics, Hydrology, or Information Engineering At least one first authored publication in a referred journal Strong record of productive and creative research, demonstrated by
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PhD studentship in the Groups “Numerical Analysis and Scientific Computing” and “Mathematics Applied to Biology” at the University of Sussex (UK). PhD project Statistical inference has proved to be
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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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and/or EU market context. The successful applicant must have strong quantitative, statistical, and analytical skills. Demonstrated knowledge of Python and Machine Learning techniques will be
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statistics and will have a background in psychology or a related discipline. They will be supervised by Dr Sam Farley, Dr Nicola Thomas, and Professor Jeremy Dawson from the Institute of Work Psychology
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subject, including: health economics, decision science, biostatistics, statistics. The candidate must have knowledge of or be willing to learn health economic evaluation. A further qualification such as an
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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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they are based on mathematical and statistical principles dating from the 1970s. Developing these pharmacometric models remains a laborious task where highly qualified staff spend large amounts of time. Aims
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generalisability compared to traditional adaptive control methods. Rigorous theoretical and statistical analysis will be carried out to prove the effectiveness of these proposed techniques. Hence, a strong