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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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obtaining, a PhD in computer science, engineering, mathematics, or a related physical sciences discipline, with research expertise in areas such as hardware-aware AI security, approximate computing, or secure
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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post by January 2026. The Requirements Essential: 1. Qualifications · A good first degree in physics. · A PhD (or be close to submission) in atomic physics or a closely related area
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will hold a PhD* in experimental particle physics or have an equivalent level of professional qualifications and experience. You must have the following essential criteria: Experience of data analysis
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Framework level 12 (PhD) plus track record of emerging independence within a research/professional environment, or alternatively possess professional qualifications and experience equivalent to PhD level plus
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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. The advertised position revolves around design and test of custom radio-frequency electronic circuits based on field-programmable gate arrays (FPGA, up to 20GHz) to drive high-speed electro-optic modulators, quasi
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of controlling small quantum computers and memories, we anticipate the development of high-speed event-driven decision making, likely to require the use of FPGAs or CPLD based hardware. In the framework of setting
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, candidate must demonstrate that they meet the following essential criteria: Hold, or about to obtain, a PhD in Power Electronics, Electronic/Electrical Engineering or closely related discipline. Recent