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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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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are looking for a Senior Research Scientist to design and develop
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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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optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a
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/chiplets/interposer/wafer-scale), quantum, superconducting, machine learning, edge computing, and security/privacy in computer architecture. Digital Logic Design and VLSI – including ASIC/FPGA design for
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and switching Familiarity and understanding of microwave and RF measurement circuit elements and instruments. Familiarity with Field Programmable Gate Array (FPGA) operation and coding Skills Needed
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on at least the following subjects: C and Python programming; digital logic and SoC on FPGA; and microcontroller systems; operating environments including embedded LINUX and other operating systems. Teaching
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people. By embracing diversity, we believe science can achieve its fullest potential. THE ROLE The iCub Tech Facility (https://www.iit.it/research/facilities/icub-tech ) at the Istituto Italiano di
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(including CIM and integrated systems) (3) Exploration of innovative hardware for DX through novel FPGA and sensors etc. (4) Advanced devices, e.g., nanomaterials, spintronics, and its AI/DX applications