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with FPGA programming used in superconducting quantum circuit experiments. Model-Free Quantum Control via Reinforcement Learning: Reinforcement learning (RL) is an emerging approach for optimizing
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are seeking a Research Associate or Engineer II to support our
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-level layer implementations - extend hardware developments to use near-FPGA DDR and HBM memories - create functional demos using networks of interest (Yolo, Resnets, LLMs, ...) - create proof-of-concept
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. The work is part of the regional project “Optimizing Renewable Energy Integration: FPGA-Based Model Predictive Control (MPC) for Grid Stability” (Ref. SI4/PJI/2024-00238) Where to apply Website https
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work environment Learn more about CQT at https://www.cqt.sg/ Job Description The CQT S14 team is looking for candidates with strong background in Software Engineering, Computational Physics or Computer
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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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Job Title: Research Associate / Research Fellow in FPGA Systems Design (SPEAR Centre) Department/School: School of Computing, Engineering & Intelligent Systems Campus: Derry~Londonderry campus
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . To develop novel inertial microfluidics and impedance-based cell sorter. To develop novel label-free CQAs based
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supported by COMMLab, 6GSPACE Lab, HybridNetLab, QCILab, TelecomAI Lab, CSAT Lab, our SW Simulators, and our Facilities. For further information, you may refer to https://www.uni.lu/snt-en/research-groups
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, depending on your skills, integrating hardware accelerators (FPGA) into "Hardware-in-the-Loop" simulations. Finally, you will actively participate in the scientific dissemination of the work through the