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topics. Candidates should have some experience working with FPGAs as well as an understanding of computer networks. Experience with both RTL and HLS design is favoured. The ideal candidate would have some
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-constrained edge hardware. Your primary responsibilities include integrating and deploying SDR platforms — such as USRPs, FPGAs, and Raspberry Pi 5 — and implementing, optimizing, and benchmarking AI and
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involves: Hardware integration – assembling measurement systems from individual components (e.g., signal generators, spectrum analyzers, antennas, mixers, SDRs, FPGAs, ADCs/DACs) Test system validation
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techniques enabling full duplex or simultaneous communication–sensing operation Implement the proposed techniques on the 6GSPACE Lab testbeds (SDR/FPGA/RFSoC based platforms and NTN channel emulators
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Qualifications (optional but advantageous) 1. Experience with FPGA programming (Verilog/VHDL) for real-time data acquisition or signal processing; 2. Familiarity with PCB design and layout (PADS, Altium, KiCad
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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, depending on your skills, integrating hardware accelerators (FPGA) into "Hardware-in-the-Loop" simulations. Finally, you will actively participate in the scientific dissemination of the work through the
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platforms, including embedded and edge computing environments (e.g., Jetson, Raspberry Pi, FPGA, neuromorphic chips) Publish results in top-tier journals and present at leading international conferences and
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, ONNX, TensorRT, FPGA) is an advantage Ability to work across disciplines - mechanics, ML, hardware, medicine We offer Multilingual and international character. Modern institution with a personal
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engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum