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-technical audiences and engage in stakeholder or end-user consultation. DESIRED CHARACTERISTICS: Demonstrated experience in models of opinion dynamics, Bayesian reasoning models, natural language processing
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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
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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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AI/ML for Object Tracking and Sensor Fusion, you will develop next-generation algorithms that power intelligent aerial systems—enabling real-time object tracking, multi-sensor data fusion, and
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mission is to accelerate the commercialisation of research for the Australian Defence Force (ADF), develop education training pathways, fast-track entrepreneurs' ideas to commercialisation, and enhance
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, or spatial relationships of objects—and to indicate when it is unsure about its input. Key expected outcomes include the creation of monitoring algorithms that identify early signs of performance issues, and
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Civil Engineering team. The ideal candidate will have a strong track record of research, relative to the career opportunity in the application of advanced materials such as fibre-reinforced polymer (FRP
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grants (as primary investigator or co-investigator) or other research funding. The ability to attract and supervise high-quality research students. A track record of achievements including prizes / awards
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for an Animal Technician to support our Research divisions across the university. In this role, the Animal Technician will support the team in achieving the objectives of LAS in delivering research support
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management decisions and mitigate risk. The successful candidate will develop the research objectives in collaboration with an experienced supervisory panel, comprised of researchers at the University