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We are looking for a Research Fellow to conduct the research for the project entitled “Manual Assembly Job Quality Inspection”. The role will focus on research and development of AI algorithms
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the regulation of emerging technologies, the societal impact of algorithmic systems, ethics and governance of AI, and the evolving role of media institutions in the digital age. The successful candidate will join
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, peripherals for DSP Applications, design and development tools for DSP, introduction to VLSI, algorithms and architecture for VLSI. The successful candidate should have extensive relevant industry experience
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for a Senior Research Engineer to work on new development of ultrathin satellite camera. The role will focus on developing algorithm for imaging technique, working with a hardware team to do various
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-forming converters and control algorithms for next-generation renewable and energy storage systems. The role will focus on control design, simulation, and experimental validation to support system stability
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digital energy system modeling, intelligent optimization, and collaborative control. Develop innovative AI models/algorithms, and research key areas such as energy generation prediction, grid and load
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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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, 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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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