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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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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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guarantees of FL. In this project, we aim at an ambitious goal - designing secure and privacy-enhancing algorithms and framework for FL and applying our designs into real-world applications. To achieve
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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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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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Level A) Based at our vibrant and picturesque St Lucia Campus About This Opportunity Join a growing, collaborative research team developing innovative quantum algorithms for decarbonisation applications
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validate wearable data algorithms, software tools and pipelines supporting large-scale, multi-cohort research. It will contribute to the ProPASS federated data analysis platform, including testing
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The successful PhD candidate will undertake research in the following areas: Develop deep learning algorithms for autonomous robotic navigation using dual-view image fusion and shadow-based visual perception
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estimation, confidence calibration, model routing, token selection, early exiting, adaptive visual processing, and efficient use of multiple foundation models. The work will combine algorithm development with
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human-centred embodied AI New methods for modelling human behaviour and interaction dynamics Multimodal temporal and predictive learning algorithms Experimental evaluation against strong contemporary