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tools, VLSI, SoC, RISC-V, compilers approximate computing, memory-centric computing, and embedded systems, (specific topics:) experience with assembly, RISC and other Instruction Set Architectures
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is therefore expect to have experience in at least one of these interdisciplinary areas: 1. Neuroscience and/or VLSI basic device physics. 2. Spike-based computing paradigms: theory and modeling. 3
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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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Experience with VLSI design (Cadence tools, Verilog/VHDL, SPICE) Knowledge of neural networks and neuromorphic systems is a strong advantage Good programming skills (e.g., Python, MATLAB) and interest in