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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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MIMIC to real-world electronic medical records from platforms, such as Cerner and Epic. Beyond data engineering, the role includes aspects of data science, such as conducting data analysis, feature
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Project (Next-Generation Solvers for Complex Microwave Engineering Problems). This project aims to design and develop physics-guided, data-driven algorithms that can accurately solve complex microwave
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process design. Collaborating with solution architects, developers, and project managers to support full information technology project lifecycle delivery. Defining Agile stories and epics, and supporting
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quantum algorithms formulating industrial problems to make them accessible to quantum algorithms mapping quantum algorithms to specific use cases and applications optimizing algorithms in the context
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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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at Griffith University. We are seeking applicants with active research interests in quantum computing, quantum algorithm/software development and applications in decarbonisation who can take full advantage
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to contemporary challenges while mentoring the next generation of leaders. Building on its strong foundation in Optics and Photonics, EECS is expanding its research into quantum algorithms and their industrial
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to real-world electronic medical records from platforms such as Cerner and Epic. Beyond data engineering, the role includes aspects of data science, such as conducting data analysis, feature engineering
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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