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Field
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Neuromorphic and AI-Optimized Processors – Design AI-specific chip architectures, including neuromorphic and domain-specific accelerators (TPUs, NPUs, FPGAs), for low-power and real-time AI processing. AI-Driven
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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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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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processing techniques using field-programmable gate arrays (FPGAs), alongside analog and digital circuit design. This research underpins biomedical applications, with a particular focus on early melanoma
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. Prior exposure to hybrid analog-digital beamforming architectures. 9. Knowledge of FPGA or GPU-based signal-processing platforms. 10. Experience in THz or sub-mm-wave measurement setups
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. Familiarity and understanding of microwave and RF measurement circuit elements and instruments Familiarity with Field Programmable Gate Array (FPGA) operation and coding Skills Needed: Knowledge, understanding
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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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implementations on deeply embedded platforms FPGA development, including test and verification Schematic entry and PCB Layout using Altium Designer ECAD/MCAD co-design using Solidworks ARM Cortex M 32-bit
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Experience with analog and digital electronics, FPGA programming, including writing firmware and software for microprocessors and embedded systems would be considered an asset. Condition of Employment