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systems to support more accurate prediction and management of environmental impacts. This is an excellent opportunity for a motivated student to work on a globally relevant environmental challenge, develop
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the development of predictive models to anticipate potential failures. Additionally, the project will facilitate the transfer of this technology to industry, while also advancing academic knowledge
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) initiative. TERN’s surveillance monitoring infrastructure supports long-term ecological and biodiversity inventory, environmental monitoring and prediction, reporting and assessment, underpinning decisions
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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for these models are then used to generate, check, verify and correct network/device configurations for correctness and security issues. The same framework will also be used to predict and plan security
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will aim to develop novel tools to aid surgical fixation or predict complications and outcomes. Each PhD student will be expected to develop innovative solutions to the problem and publish approx. 3-4
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ability to predict locust band movement. This project will focus on modelling the collective movement of locust hopper bands (thousands to millions of organisms). We will improve on current models through
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to lead to improved predictive design of biomass crops for the production of sustainable aviation fuel. The postdoc will also co-supervise PhD students and Honours students. To be successful you will need
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well as an informed understanding of the losses that have already occurred. If possible, the project could extend to predicting (based on past experience) which portions of the Adelaide Park Lands might be most at risk