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to contribute to research on hybrid numerical-exponential asymptotic methods and their applications at the University of Sydney Base Salary, Academic level A $105,117 - $121,054 p.a + 17% superannuation About the
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translating these into field-deployable species identification protocols using qPCR and long-read sequencing technologies. You will also support method validation and standardisation across diverse fishing and
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to the development of new concepts, methods, and scalable systems for networked digital twins that support autonomous decision infrastructure for adaptive action with human oversight. This project is part of
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and AI/ML. In this role, you will advance innovative research that apply AI/ML to develop methods and tools to improve crop performance and resilience, contributing to solutions for global food security
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methods (for example, learning-based, network-based, or agent-based approaches) to analyse dynamic decision behaviour, rather than focusing solely on predictive or optimisation performance prepare and
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methods for DSGE/OLG models and dynamic programming models. Familiarity with simulation and calibration methods. Strong skills in econometrics, including familiarity with method of moments and maximum
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impactful scholarship within the Institute for Musculoskeletal Health. Working under broad supervision, you will exercise sound judgement in selecting appropriate research methods, planning and prioritising
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of A/Prof Patrick Burr, please visit https://www.patrickburr.com/group At UNSW, you will join a vibrant materials research community within the School of Mechanical and Manufacturing Engineering and the
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an operating budget of around $18 million. For further information about our School, please visit - https://www.unsw.edu.au/engineering/our-schools/electrical-engineering-telecommunications Skills & Experience
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skills in mathematics and statistics, including in application of computational methods. Demonstrated interest/experience in population health and observational science Conduct research/scholarly