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-level layer implementations - extend hardware developments to use near-FPGA DDR and HBM memories - create functional demos using networks of interest (Yolo, Resnets, LLMs, ...) - create proof-of-concept
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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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communication skills to collaborate smoothly with international colleagues. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA
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architectures. Knowledge of embedded systems. Knowledge of developing systems with systems-on-a-chip (SoC) and FPGAs. Knowledge of scheduling is desirable. Motivation to pursue a doctoral degree and contribute
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-funding from industry and academic partners. You can read more about NICE on https://www.ntnu.edu/nice. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research
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more about NICE on https://www.ntnu.edu/nice. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within the framework described above
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-design with accelerators (FPGAs, GPUs, near-memory systems) to achieve real-time, energy-efficient AI for high-tech industry applications. Work with leading companies like ASMPT and shape the future of AI
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the development of scalable software tools and pipelines, potentially leveraging GPU/FPGA accelerators. Our aim is to build next-generation molecular atlases for chronic diseases and to improve patient
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of the candidatures. Further information at https://www.dges.gov.pt/en/pagina/degree-and-diploma-recognition Where to apply Website https://www.it.pt/ Requirements Research FieldEngineering » Electronic
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leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence will enable the rapid evaluation