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and statistics, with expertise spanning time series analysis, Bayesian inference, financial econometrics, and data analytics. As home to one of the strongest forecasting research groups worldwide, we
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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, they will have prior knowledge of infectious disease modelling, Bayesian inference methods and optimisation methods. They will have a developing research profile, with a demonstrated ability to publish
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metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming
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and the School of Computing Technologies to advance research on bushfire evacuation and emergency planning and preparedness in hazard events. You will support the research aim and objectives of the ARC
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developments. Demonstrated ability to undertake high quality academic research and conduct independent research with limited supervision. Demonstrated track record of publications in high-impact journals and
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potential of high-speed rail (HSR) in shaping Australia’s urban future. As a Level A Research Fellow, you will undertake high-quality, independent research aligned with the project’s objectives, with a focus
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, epidemiology, and/or dementia. Experience with non-pharmacological clinical trials conducted in older adults and those with cognitive impairments. Demonstrated track record of publications and conference
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students and contributing to the academic life of the School. Managing project timelines and deliverables to ensure research objectives are met. Sharing research outcomes through conferences, seminars, and
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partners. Supervising postgraduate research students and contributing to the academic life of the School. Managing project timelines and deliverables to ensure research objectives are met. Sharing research