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of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard
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/linux shell. Must include familiarity with a terminal-based text editor. One year or more of experience with firmware development with FPGAs. Experience working with bit-level operations on files(e.g
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-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high
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. Strong communication skills in English. Proven experience in USRP, FPGA, NS-3, and NSF PAWR platforms. Preferred Qualifications Previous experience with DoD sponsored research projects. Relevant practical
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systems including bare-metal embedded systems, RTOSes, FPGAs, and embedded Linux. You will have opportunities to develop tools, techniques, and processes to solve some of the most difficult software
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hardware systems including bare-metal embedded systems, RTOSes, FPGAs, and embedded Linux. You will have opportunities to develop tools, techniques, and processes to solve some of the most difficult software
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hardware systems including bare-metal embedded systems, RTOSes, FPGAs, and embedded Linux. You will have opportunities to develop tools, techniques, and processes to solve some of the most difficult software
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provide a performance or efficiency advantage, and determine scenarios where conventional AI accelerators (such as embedded GPUs or FPGA-based accelerators) remain more appropriate due to data
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, single-atom catalysts), analytical instrumentation (including electronics and LabVIEW FPGA programming), finite element modelling using COMSOL Multiphysics, and in-situ/operando spectroscopy, among other
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mitigate those that emerge. Analyze FPGA hardware and software interface specifications described via VHDL or Verilog to ensure it meets system requirements. Analyze software test and evaluation techniques