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Associate to join the Parallel Systems Lab (https://psl-ntu.github.io ) at NTU’s CCDS. The role focuses on designing, developing, and evaluating efficient, scalable systems for modern multi-agent HPC-AI
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algorithm code base including noise reduction, acoustic echo cancellation, automatic gain control, voice activity detection, packet loss compensation and other audio enhancements Software development of new
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prediction algorithm and molecular dynamics simulations. For more details, please view https://www.huilingshaogroup.com/. We are looking for a Research Assistant to design and execute independent and
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collection from both experimental and numerical sources, gathering relevant test and simulation data available globally, organizing the collected data to support model and control algorithm development and
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a Research Fellow to support
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description languages. Develop and optimize EDA workflows for processor and accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee The project aims to develop robust, intelligent
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. The successful candidate will assist in developing novel algorithms and integrating them into robotic platforms, helping to push the boundaries of embodied intelligence in both research and practical deployment
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. The successful candidate will assist in developing novel algorithms and integrating them into robotic platforms, helping to push the boundaries of embodied intelligence in both research and practical deployment
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applications including asset pricing, volatility modelling, risk management etc. Developing the framework of economic world models, with generative AI algorithms such as diffusion models and theory-informed