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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
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systems for sensor alignment, motion control, and structural stability; collaborating on the assembly, integration, and optimisation of laser imaging platforms; conducting testing and validation to ensure
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frameworks (MOFs), and related materials using hybrid classical-quantum algorithms. A key component of the role involves using first-principles methods that capture strong electronic correlations, such as DFT
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of photonic single protein sensors to observe, control and understand protein behaviour, advancing knowledge at the interface of quantum science and the life sciences. As a Level B Research Fellow, you will
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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research
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programming and data analysis. Interest in developing methods, algorithms or software. Evidence of publications in high-quality peer-reviewed journals. Excellent communication skills. Experience
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on clusters and high-performance computing infrastructure), Information Retrieval methods, Machine Learning algorithms, wrangling large-scale datasets, and showcasing the research results. The ability to work