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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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(OpelRT or Typhoon), electrical system design for better efficiency and system resiliency, and energy management algorithm development using MATLAB/Simulink for marine microgrid applications. Knowledge
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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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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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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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Engineering (EEE), helping to develop algorithms and systems for disaster mapping and understanding geohazards in collaboration with space industries and responding agencies around the world. They will
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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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Responsibilities: Conduct individual research within the designed project: process data, develop research methods, build and evaluate computer vision and machine learning algorithms empirically. Author research
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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