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Field
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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support to faculty and administrative staff. Continuously monitors OIRDS applications and program scripts to distribute workloads efficiently across servers, ensuring optimal system performance. Implements
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research and teaching within The University of Queensland (UQ) School of Electrical Engineering and Computer Science (EESC). The QDA brings together academia, industry, and government to accelerate
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courses with minor algorithmic components and primarily programming courses with a focus on bioinformatics methods. Such graduate courses seek experienced bioinformatics, biotech, and data science
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preferred. Experience utilizing computer vision algorithms and frameworks, including medical image classification, feature matching, edge detection, image segmentation, and deep learning models like
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signal processing algorithms on FPGA, optimized to significantly improve the resolution of real-time energy measurements made by the ATLAS Liquid Argon Calorimeter system. Use novel high-level synthesis
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mix of algorithm engineering and formal methods, alongside more traditional software engineering activities. This project involves developing software which is both mathematically rigorous, and
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qualified candidates to apply for a postdoctoral research position. The research will focus on statistical modeling, machine learning and trustworthy AI to develop effective and efficient algorithms
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, and ways of applying such foundational analysis for quantum technological or social benefit (e.g., through designing protocols or algorithms for, or building understanding of, quantum computing