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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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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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(e.g., hardware trojans, side-channel exposure). Co-develop testbenches for hardware simulations and chiplet-level threat modelling. Collaborate closely with FPGA and IC prototyping teams to deploy AI
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Reconfigurable/Spatial computing architectures, such as FPGAs, CGRAs, and AI accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs
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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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, single-atom catalysts), analytical instrumentation (including electronics and LabVIEW FPGA programming), finite element modelling using COMSOL Multiphysics, and in-situ/operando spectroscopy, among other
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signal processing algorithms on FPGA, optimized to significantly improve the resolution of real-time energy measurements made by the ATLAS Liquid Argon Calorimeter system. Use novel high-level synthesis
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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