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water, rapid degradation in the gastrointestinal tract, or limited bioavailability at target tissues. The successful candidate will address these barriers by developing oral delivery platforms for fragile
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Apply now Job no:534307 Work type:Full Time Location:Sydney, NSW Categories:Post Doctoral Research Associate Employment Type: Full-Time (35-hour week) Duration: Fixed-Term Contract until 31 December 2027 Remuneration: (Level B) $127,947 - $150,830 + 17% superannuation Location: Work from either...
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 17 days ago
to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig economies and prediction markets. Opportunity to supervise research students and work as part of a
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, mathematical psychology (computational modelling) and/or human factors methods and related statistical techniques (including Bayesian hierarchical methods) Experience with the development and application
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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 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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international scale and will contribute to improving the grains industry’s ability to understand and manage the economic risks of the target species. Activities will ensure trans-disciplinary collaborations