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Field
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include the development and implementation of control algorithms on FPGA platforms, experimental testing using Hardware-in-the-Loop (HIL) setups, data analysis, and preparation of scientific publications
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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research
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strategies for programming, modeling, and integrating reconfigurable/spatial architectures, such as FPGAs and ML accelerators, within heterogeneous ICT ecosystems.Reconfigurable and Spatial hardware, such as
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National Institute for Research and Development in Microtechnologies - IMT Bucharest | Romania | 5 days ago
applications using FPGA technology. Romanian citizen or citizen of another European Union member state; Resident in Romania; Very good (written, spoken) knowledge of Romanian and English. Specific Requirements
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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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. During the project, these modules will be emulated by RF FPGA boards designed at the beginning of the project. The first objective of the project will therefore be to propose a very low cost wireless link
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optics, ideally, 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
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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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skills in designing advanced neural networks (including Vision Transformers, Graph Convolutional Networks, and Spiking Neural Networks), hardware implementation of algorithms for reconfigurable FPGA
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a suit of fast electronics (FPGA) that will allow the following achievements: 1) To rapidly repeat single-particle experimental dynamical trajectories to gain sufficient counting statistics in order