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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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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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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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), 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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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and
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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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. Demonstrated ability to communicate and interact with a diverse range of stakeholders and students. An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant
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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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at the Fertility & Research Centre, you will undertake innovative research in the field of oocyte and ovarian biology, with the overall objectives of defining new mechanisms regulating female fertility and
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judgement and initiative. Excellent written and verbal communication skills, with a high level of attention to detail. An understanding of and commitment to UNSW’s aims, objectives and values in action