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landfill. We are looking for a passionate researcher who can undertake mechanical design, design and conduct experiments, learn quickly, solve complex problems, develop innovative solutions, and contribute
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of emerging spatial omics technologies. Working within the Sydney Precision Data Science Centre, they will help develop open-source tools that improve our understanding of spatial organisation within complex
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Associate (Level A) to join a new research initiative focused on next generation digital twin capability for complex urban and infrastructure and urban systems. Working under the supervision of Professor
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digital twin capability for complex urban and infrastructure and urban systems. Working under the supervision of Professor David Levinson , the successful candidate will contribute to the development
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digital twin capability for complex urban and infrastructure and urban systems. Working under the supervision of Professor David Levinson , the successful candidate will contribute to the development
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modelling approaches, including integrated assessment modelling, material-flow analysis and life-cycle assessment, while developing robust and reproducible workflows using large and complex datasets
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ability to contribute original ideas to collaborative projects. Strong analytical and problem-solving skills, including the ability to develop rigorous mathematical proofs and perform complex calculations
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to complex ecological networks design and implement novel coordinate-independent numerical algorithms utilising automatic differentiation and pseudo-arclength continuation methods to identify contact points
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-autonomous systems, and their applications to complex ecological networks design and implement novel coordinate-independent numerical algorithms utilising automatic differentiation and pseudo-arclength
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reduction for data with complex structures, including but not limited to, multi-way clustered, spatial-temporal, and network dependence. The successful applicant will be jointly supervised by CIs Linh Nghiem