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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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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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scheduling, resource allocation, and workload placement algorithms for distributed AI training; Improve the communication efficiency, scalability, and reliability of decentralised training frameworks; Evaluate
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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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strategies. Carbon-Aware and Sustainable Scheduling Algorithms: Research Focus: Creating algorithms that factor in carbon intensity and renewable energy availability to minimize environmental impact. Skills
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that operate at scale and complexity. You will apply strong software engineering practices to solve complex technical challenges, helping to bring advanced algorithms into real-world operational environments