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twin to enable real-time simulation and visualization. The system’s performance will be evaluated in terms of prediction accuracy, computational efficiency, and usability to ensure it provides
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twin to enable real-time simulation and visualization. The system’s performance will be evaluated in terms of prediction accuracy, computational efficiency, and usability to ensure it provides
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segmentation, enhancement, and reconstruction. Funded as an Australian Research Council Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic
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Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering , with demonstrated knowledge in machine learning, algorithms, and programming. Prior exposure to reinforcement learning
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Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering , with demonstrated knowledge in machine learning, algorithms, and programming. Prior exposure to reinforcement learning
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simulation techniques, as well as available data from scientific publications, citations, and science career trajectories. The goal is to design better incentives for scientists to produce their best work
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simulation techniques, as well as available data from scientific publications, citations, and science career trajectories. The goal is to design better incentives for scientists to produce their best work
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knowledge and experience in computing, algorithm development, computer simulation, and/or artificial intelligence and machine learning; has excellent communication skills and a demonstrated ability to
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ability to learn? The precise question will be developed with the student. A project may combine evolutionary and behavioural game theory, multi-agent reinforcement learning, agent-based simulation, or
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ability to learn? The precise question will be developed with the student. A project may combine evolutionary and behavioural game theory, multi-agent reinforcement learning, agent-based simulation, or