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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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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 hail climatology for France, Switzerland and Northern Italy, including hail swaths per event across more than a decade. Design, develop and train geostationary satellite-based hail algorithms using
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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
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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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responsibilities will be to: conduct high-quality research in intelligent sensing and control for complex project environments develop and implement AI-based algorithms (e.g., GNNs, deep reinforcement learning
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: Research: Development and validation of predictive maintenance algorithms for solar farms. Interface with industry partners for knowledge sharing and feedback. Play a key role in reporting to the funding