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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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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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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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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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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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, materials, and processes. Manage waste disposal in line with standard procedures. Assist with stock receipt, distribution, and stocktaking of food, bedding, and supplies. Support ordering of consumables
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techniques for near-field microwave systems, design and test antennas/antenna arrays for near-field microwave imaging, model and simulate antenna and algorithm performance, participate in lab testing, write
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workflow, workload distribution, leave planning, performance development, and induction and training activities. Stakeholder Liaison & Conflict Resolution Act as a central liaison, facilitating clear
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partners. About TERN TERN is an Australian Government initiative to enhance Australia’s environmental research effort. TERN coordinates a nationally distributed set of instrumentation that contribute to data
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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