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into the co-design of ultra-low-power AI hardware architectures tailored for edge computing applications. The research aims to develop neuromorphic processors, FPGA/ASIC-based AI accelerators, and intelligent
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. Specific requirements of the candidate Applicants should have a strong background in cryptography, FPGA design, or embedded systems. Experience with hardware design (e.g., Verilog/VHDL), post-quantum
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/VHDL and C/C++ • Understanding of machine learning frameworks (e.g., Scikit-learn, TensorFlow Lite) • Demonstrated interest or experience in energy systems, NILM, or edge AI • Experience in
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(e.g. quantum logics and memories, sources and detectors, etc), CMOS or FPGA technology, micro/nanofabrication, low noise high-frequency characterisation and measurements. C2 Knowledge of using high