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Field
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quantum sensing technologies (e.g., Rydberg atomic sensors) for wireless communications and sensing. Key Responsibilities: Develop quantum-related theories, models, and algorithms for various communications
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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validation of advanced algorithms for disease detection, contributing to Australia’s data-driven crop health monitoring systems. This work will support sustainable crop production and enhance national
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scalable quantum algorithm development and quantum-HPC codesign. What is Required: PhD in Computer Science, Computational Science, Applied Mathematics, or a related field awarded within the last five years
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validate advanced 5G features such as network slicing, MEC and xApp/rApp. Contribute to the development of innovative solutions and algorithms to enhance 5G network capabilities. Work closely with
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, TensorFlow) and Python ML libraries (e.g., NumPy, OpenCV, scikit-learn). Experience implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based
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implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based approaches. Proven ability to design and run rigorous experimental frameworks, including
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during pandemics. Populating these instances with real-world data we would then develop novel algorithms to solve them. The selected candidate would disseminate their research by publishing in top-tiered
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include: Lead original research in multimodal and causal AI for health; design, implement, and rigorously evaluate algorithms and full pipelines. Build reproducible research pipelines and maintain reliable
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research in the field of AI for healthcare autonomous systems. Activities on non-healthcare systems could occasionally be requested. • Undertake research from algorithm development to real time