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and navigation algorithms for robot in complex environments. Key Responsibilities: Responsible for development of robust and reliable sensor fusion algorithms for localization and navigation algorithms
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different approaches. • Construct mathematical models that capture meaning problem formulations, and identify why existing methods are insufficient. • Develop solutions in the form of algorithm design
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to develop an online framework for assessing fatigue, structural integrity, and operability of multiple floating offshore wind turbines, supported by computationally efficient learning algorithms with
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exploration and relevant disciplinaries Key Responsibilities: Develop novel imaging algorithms and validate them through synthetic experiments Extend these methods to real-world experiments to address practical
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Odometry (LIO), Visual-Inertial Odometry (VIO), and multi-sensor fusion for UAVs and other agile platforms. The Research Fellow will develop high-performance, robust estimation algorithms that operate
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optimisation, algorithm design and high-performance computing, with application to airport innovation. Successful candidates will join an active group of Principal Investigators and researchers to work within
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, and learning capabilities Develop and optimize data-driven algorithms for anomaly detection and incident response Implement scalable, modular, and containerized solutions compatible with platforms
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research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in
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on computer vision. The role involves developing and advancing novel algorithms for emerging challenges in computer vision, including continual learning and few-shot learning. The candidate is also expected
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the research on Integrated Sensing and Communications (ISAC) with Reconfigurable Intelligent Metasurfaces (RIS). Key Responsibilities: Design the algorithm for Integrated Sensing and Communications