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of novel edge-assisted computation offloading strategies that leverages edge intelligence. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core
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will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core responsibility is to build and validate these offloading strategies, complete with Python
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responsible for the end-to-end investigation of novel federated learning strategies for causal inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real
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high-frequency ultrasound imaging platform, including hardware-software integration and advanced signal processing algorithms for structural and functional imaging, in order to deliver clinically
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intelligence (AI), and high-performance computing (HPC), as well as quantum-inspired algorithms and quantum machine learning (QML). Responsibilities: Lead and contribute to applied quantum computing research
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learning algorithms. Collaborating with industry partners to understand operational requirements and developing AI pipelines for analysing visual and sensor data collected during inspection processes
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. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core responsibility is to build and validate these offloading strategies, complete with Python
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industry value in the short to medium term. This includes applications combining quantum computing, artificial intelligence (AI), and high-performance computing (HPC), as well as quantum-inspired algorithms
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Investigator (PI), Collaborator and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: Develop and deploy multimodal AI algorithms for fire
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the development, implementation, and testing of signal processing and imaging algorithms for high-frequency ultrasound structural and functional imaging. Support experimental setup, data collection, phantom