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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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) general-purpose hardware such as accelerators for AI and ML, high-performance computing, low-power edge computing, quantum computing, cybersecurity, chiplets, and CPU, TPU, GPU, and FPGA systems; or (2
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as Instructors in the following areas: Circuit Design Digital Systems Control Systems Microprocessor-based Embedded Systems Communication Systems VLSI Design FPGA-based Embedded Systems Lasers
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We invite applications for multiple Postdoc positions in the Accelerated Connected Computing Lab (ACCL) at KAUST! The new research group, supervised by Suhaib Fahmy explores hardware acceleration