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superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who discover them About Us The Department of
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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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), 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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superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who discover them The Opportunity The Department
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internationally. Key responsibilities include: Developing and standardising methods to detect and analyse microplastics in soils and livestock. Establishing an Australian data inventory on microplastic
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collaborative networks both nationally and internationally. Key responsibilities include: Developing and standardising methods to detect and analyse microplastics in soils and livestock. Establishing
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to prioritise a range of tasks and meet deadlines. An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines. Knowledge of health and
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research profile in peptide chemistry and ion channel biology. Our laboratory explores the venoms of arthropod predators to discover peptides that modulate the activity ion channels involved in human disease
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
until 30 June 2027 to contribute to the development of artificial intelligence systems which reliably and effectively detect anomalies in the workspace based on vision and/or natural language modalities