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differential equations on existing published datasets. In the latter stages, this project will also involve the generation of new data in emerging applications. As a PhD student, you will address the forecasting
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to: Enhancing Small-Signal Stability in Inverter-Dominated Grids, Examining the effectiveness and uptake of electricity pricing mechanisms, Forecasting the future of indoor air, Role of storage in the energy
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to forecast disease burden under alternative vaccination schedules and to assess the impacts of completed vaccination programs against baseline transmission counterfactuals. This project will build on existing
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into visions and scenarios for the energy transition Mapping/visualising Indigenous knowledge of climate and weather to support energy forecasting and planning More broadly, applicants are encouraged
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term