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conceptual knowledge and/or practical experience in topics such as agent-based modelling, bayesian statistics, causal inference, data visualisation and graphical interfaces, geospatial data analysis, high
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biostatistical training and skills, including longitudinal and correlated data; familiarity with advanced analytics including machine learning, Bayesian methods, and causal inference also desired. Strong written
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry, etc. We work on a variety of learning problems, especially those involving supervised
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high information content Flow MRI datasets with physics based modelling and Bayesian inference to determine constitutive models for non-Newtonian and other complex fluids in situ. The project will
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conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and
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used will the information-theoretic Bayesian minimum message length (MML) principle. Student cohort PhD, possibly Master’s (Minor Thesis) or Honours URLs/references Chen, Li and Gao, Jiti and Vahid