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availability characteristics. A key enabler for this research is openwifi, IDLab’s open-source full-stack Wi-Fi implementation that runs on system-on-chip FPGA platforms ( https://github.com/open-sdr
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networks. Experience securing research funding, managing research programs, and building academic partnerships. Experience with embedded systems, FPGA/RTL, and RF technologies, and CUDA coding for GPU
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programming languages, e.g. C/C++, Python. Experience with Machine learning technologies (e.g., LLM, and Machine Learning) is an advantage Experience with Hardware, e.g., FPGA acceleration, is an
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programming languages, e.g. C/C++, Python. Experience with Machine learning technologies (e.g., LLM, and Machine Learning) is an advantage Experience with Hardware, e.g., FPGA acceleration, is an
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Australian National University | Canberra, Australian Capital Territory | Australia | about 20 hours ago
. in C++, C, MATLAB and/or VHDL for FPGA configuration); has fundamental knowledge and demonstrated ability to conduct research in wireless communications and/or signal processing; preferably has
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related discipline. 2. Have or be about to obtain, a PhD in a relevant subject. 3. Have relevant research experience in either embedded systems design or FPGA or ASIC hardware design, along with
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/ FPGA design skills. Good understanding of CPU architectures Relevant academic or industrial research experience in either: Hardware accelerated custom computing. RISC-V design/prototyping. This is
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in hardware–software integration, GPU/FPGA acceleration, or translation of research into prototype systems would be an added advantage. Ability to work independently while contributing effectively in
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/analysis and scripting for instrument control (e.g., Python/Matlab/LabVIEW) Applicants preferably should have experience in real-time processing or FPGA-based prototyping or embedded sensing
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implementing high performance signal processing algorithms on resource constrained hardware Experience using FPGA development tools such as Vivado and LiteX Experience using GPU compute frameworks such as