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for a Research Fellow to develop advanced wet-spinning processes for conductive polymers, enabling next-generation fabric-based sensors and thermoregulation systems. The role will focus on the design and
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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on AI-driven end-to-end autonomous driving algorithms. Key Responsibilities: The research fellow will be leading the development of AI-driven end-to-end autonomous driving algorithms. The work will
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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on rapid and accurate quantification of disasters using remote sensing and space geodesy. They will also advance InSAR processing algorithms to optimise change detection capability in Southeast Asia, where
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on results. Document development processes, models, and algorithms for future reference and reproducibility. Ensure compliance with data privacy and security regulations in all development activities. Any
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, test & measurement instruments, sensors/sensor networks, and PLC systems is preferred. Prior experience in standards-based testing and DC grid applications would be advantageous Job Requirements
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experiential learning programs. We invite applications from scholars whose expertise encompasses one or more of the following areas: Human-AI (or AI agent) interaction, human-machine communication, algorithmic
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work with our team to conduct research on the development of image analysis algorithms and AI methods for hardware security evaluation. The roles of this position include: Study literatures on multi
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and Autonomous Vehicles • Beyond 5G Circuits and SiP/SoC • IoT Sensor and Flexible Electronics Circuits and Systems • Hardware and Physical-Cybersecurity Circuits and Systems • Quantum Circuits and