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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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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 design and verification (HDL: VHDL/Verilog, simulation tools). · Hands-on experience with FPGA prototyping or hardware/software co-design is highly desirable. · Good communication
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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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work within the ADAPTING PEPR project, funded by France 2030 and led by the ANR (French National Research Agency). Within this project, the ASIC team at IETR aims to develop a distributed platform for
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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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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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. 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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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