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; University of Sussex | The City of Brighton and Hove, England | United Kingdom | about 17 hours ago
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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-visible-spectrum reflectance). This project brings together statistical modelling of the data-generating process with machine learning, including deep learning, techniques, to model and predict bumblebee
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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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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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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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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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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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strong candidates without postgraduate qualifications will also be considered. Experience conducting experimental studies with human participants. Good quantitative research skills including statistics