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digital de señal y lógica de control en tiempo real. - Optimización de uso de recursos, consumo energético y rendimiento temporal (timing closure. Implementation and optimization of hardware logic using
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background in AI-enabled signal processing and machine learning algorithms, have experience with embedded platforms (e.g., NPU, FPGA, ARM Cortex-M), be proficient in programming languages like C, C++ and
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https://www.imec-int.com/en/work-at-imec/job-opportunities/senior-sensor-fusion… Requirements Research FieldEngineering » OtherEducation LevelPhD or equivalent Research FieldComputer science
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optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a
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with FPGA programming used in superconducting quantum circuit experiments. Model-Free Quantum Control via Reinforcement Learning: Reinforcement learning (RL) is an emerging approach for optimizing
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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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supported by COMMLab, 6GSPACE Lab, HybridNetLab, QCILab, TelecomAI Lab, CSAT Lab, our SW Simulators, and our Facilities. For further information, you may refer to https://www.uni.lu/snt-en/research-groups
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security concepts. Experience with embedded platforms, FPGA development, or hardware-accelerated computing will be considered an advantage. Familiarity with technologies for deploying AI on constrained
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of distributed MIMO, and/or coordinated multi-AP operation (under study in the Wi-Fi 8 standardisation workgroup), using Hardware Description Language on FPGA, based on the open-source openwifi project (https
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optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a