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Field
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focused on the challenge of accelerating ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track
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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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software and hardware (knowledge on working with FPGAs and ASICs will be preferred). Achievement of the expected progression within Post Doc and Senior Post Doc is transferable between the Irish HEI’s
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know the fundamentals of quantum computing. It is also expected that the participant has knowledge to work on diverse software and hardware (knowledge on working with FPGAs and ASICs will be preferred
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hardware design (Verilog/VHDL), FPGA-based acceleration, etc. Experience with deep learning frameworks like PyTorch, Keras, or TensorFlow, and tools such as Jupyter Notebook, is expected. A strong foundation
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., GNURadio, Matlab, LabView), and/or FPGA development (e.g., VHDL) is a plus. You are a team player and have strong communication skills. You have a high proficiency in oral and written English. You comply
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of using FPGA tools and provide them the necessary training to use the ASIC tools. The team at Cambridge consists of three investigators: Prof. Robert Mullins (PI), Prof. Timothy Jones and Dr Rika Antonova
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, Computer Science, Experimental Subatomic Physics, or related fields. Relevant research experience in firmware programming (e.g. with VHDL) and resources optimization for specific FPGA architectures
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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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will be involved in crafting and applying high-accuracy algorithms for a Spiking Neural Network (SNN) processing unit, to be executed on FPGA and ASIC. As a Postdoc, your key responsibilities will be