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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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, instrumentation, electronics, FPGA technologies and signal processing. The position will involve strong interactions with national, European and industrial partners and will contribute to the scientific
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Model. In parallel, the group is engaged in hardware activities related to the development and implementation of new FPGA-based trigger processors, both for the operation of the ATLAS muon spectrometer
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, FPGA-based instrumentation, and photon-resolved microscopy. In this role, you will contribute to the ERC Proof of Concept (ERC PoC) project BrightMind Grant Agreement n.101286935: An Integrated Hardware
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 5 days ago
candidate should have familiarity with some combination of superconducting detector testing, microwave engineering, digital signal processing and FPGA firmware design, and data analysis in modern programming
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3rd, 2026 Closing Date for Applications: August 23rd, 2026, 23h00m (Lisbon Time) Key words: #photonics #optics #setupdevelopment #nanofabrication #nanophotonics #quantumphotonics #FPGA Overview
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, knowledge of electronics, Python and FPGA (VHDL) programming, interest in working with experts on a cross-institute collaboration project, very good written and spoken English language skills, and excellent
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the implementation of the system's data plane, applying embedded design principles to high-performance networking: SmartNIC & FPGA Programming: Implement real-time flow steering logic on devices like
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. Experience with heterogeneous and parallel computing technologies, programming models, or accelerators, including CPUs, GPUs, FPGAs, and emerging computing technologies. Familiarity with quantum software
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networks, RNNs, LLMs) or in the deployment of such algorithms. Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators