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aims to leverage quantum computing to address these challenges, focusing on developing novel quantum algorithms to enhance mRNA sequence design You will be co-supervised by leading experts in AI, data
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This project develops quantum-AI tools to optimise urban hydroponic food systems and evaluate their therapeutic impact on student well-being. The expected outcome is to create quantum-reinforced AI
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challenges: Successful candidates will contribute to one or more of the following research domains: development of autonomous navigation and path planning algorithms for lunar terrain traversal and regolith
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Antenna Design: Developing innovative antenna systems compatible with metal detector coils, optimized for tasks such as minimum-metal landmine detection and deep object detection. Low Power, Compact
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years of continuous R&D. It transforms pioneering Australian research into a market-ready product for transport planning. About the opportunity Lead research and development of advanced computer vision
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product for transport planning. About the opportunity Lead research and development of advanced computer vision models, multi-object tracking, and post-processing methods to improve traffic video analytics
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data on homeowner retrofit needs and preferences. Undertaking research trials to test and refine the AI algorithms used in our platform. Meaningful assistance in research and policy development with a
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of the postdoctoral researcher will include: To work closely and proactively with Prof Anton van den Hengel to scope and develop research ideas. To develop algorithms, machine learning models, Python modules
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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ideas. To develop algorithms, machine learning models, Python modules, demonstrators and training pipelines for publication and translation into commercial products that can be widely and reliably adopted