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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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of this role is to support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep
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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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, 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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of performance, speed, and precision. Key Responsibilities: Design and implement genAI models for embodied AI systems. Develop and optimize deep learning algorithms to enable robotic arms to perform complex tasks
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operation. Develop modular architectures for multi-agent coordination, sensing, and communication. Integrate sensor suites, flight controllers, and swarm coordination algorithms into UAV platforms. Conduct
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on complex system subject to various constraints. Design and develop real-time algorithms. Simulate and evaluate system performance. Document research outcome to publish at international conference / journal
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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The Continental-NTU Corp Lab is a strategic research collaboration between NTU and Continental, focusing on developing technically advanced solutions in the areas such as sustainable materials