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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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emerging models by collaboratively exploring various computation models leveraging physical devices properties. This PhD work will focus on FPGA devices in order to build an accelerated spiking neural
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hardware instructions. Proficiency of FPGA development tools such as Vivado, Quartus, or similar for prototyping. Preferred Additional Skills: Strong understanding of ASIC/FPGA design flows, including
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