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the fields of uncertainty quantification, data assimilation and optimisation under uncertainty, complementing data-driven approaches such as physics-informed machine learning. We will start by focusing
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Australian grain growers face increasing challenges from seasonal uncertainty, rising input costs, and climate variability. This PhD project offers a unique opportunity to be at the forefront
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dissemination nexus for Futures research and activity. It is our response to this moment where we live with elevated future uncertainties in a changing climate, in our applications of emerging technologies, and
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and cost outcomes, which include but are not limited to Statistical (risk) modelling, Model calibration and uncertainty estimation, Causal learning for explainable machine learning, Transparent