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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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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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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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optimization problems Develop mathematical modeling framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data
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; Develop models and algorithms for energy-aware scheduling, workload prediction, and performance–energy trade-off optimization; Investigate network system energy efficiency, including traffic scheduling
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, including Computational Fluid Dynamics (CFD) for thermal analysis and energy simulation for consumption modelling. Design, train, and implement advanced machine learning and deep learning algorithms
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on novel agent architectures, including tool-use methodologies, advanced planning algorithms, and multi-agent collaboration for simulation-based optimization. Innovate & Conceptualize: Design and