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disease patients using radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical
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postdoctoral researcher with: A PhD (or near completion) in Computer Science, Computational Biology, Mathematics, Bioinformatics, or a related discipline. Proven expertise in machine learning and algorithm
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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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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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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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experience. Experience in analysing large, complex ecological or biodiversity datasets. Strong proficiency in statistical modelling, including experience with species distribution models, community ecology
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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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large-scale distributed computing systems (e.g. Edge, IoT), evidenced by involvement in several industry projects. Technical Experience: Exposure to Go, Python, Java or equivalent. Experience with
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. Harnessing technology, we thrive as a distributed yet connected community, welcoming and engaging with people across Australia and the world. Learn more about the Faculty of Science and Health here . Learn
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distribution across multiple HSR scenarios. You will work alongside a team of internationally renowned experts in transport and urban planning, including Associate Professor Liton Kamruzzaman, Professor Hai Vu