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energy transition. This role focuses on the development and application of advanced forecasting and scenario analysis methods within in-house grid modelling platform to inform the design of Australia’s
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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and develop an optimal (efficient, cost effective, easy to use) technological operational method for detecting and estimating wallaby populations and dynamics. What you’ll do: Develop a technological
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analysis to estimate ecological parameters, and implementing models in Matlab. You will work closely with a diverse team of around 20 field ecologists and modellers, as well as international collaborators
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Sciences, Political Sciences, Computer Sciences, or Engineering, with a strong track record (relative to opportunities) in research on disaster management, extreme contexts, and resilience including a body
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many-body methods for multi-valence-electron atoms, with a focus on transition metals of interest for spin-crossover metal-organic frameworks (MOFs). The applicant will be involved in the development
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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