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This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems. The research will investigate how quantum
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well-being and mental health, to the future of music, and inclusive communications for CALD communities – we have an exciting research program for those ready to take the next step in their career and
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that can better utilise distributed computing resources while reducing energy overheads and supporting more sustainable AI infrastructure. The successful candidate will have the opportunity to work on real
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at sites around Australia and engaging with a range of key stakeholders as part of our market validation program. In collaboration with a strong existing scientific and industrial userbase, we will validate
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undertake research in quantum algorithms, error correction, and fault-tolerant quantum computing architectures, with applications to decarbonisation. Based within the Queensland Quantum Decarbonisation
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Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind
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imaging technologies. Our research portfolio spans development of new treatment ideas, clinical trials, preclinical imaging, device development, MRI-Physics, radiation therapy physics, and computational
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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application of these AI algorithms to imaging held in data banks such as BioHEART. Working in an interdisciplinary manner will bring together medical, computer science and engineering mindsets to apply a smart
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experiments for months before the value of output y is measured for some given input x. This creates an exciting challenge for AI researchers to develop smart algorithms that can find the optimal value of input