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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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, 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
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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
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virtual reality systems, code language models, high-level synthesis, Bayesian neural networks, and FPGA-based acceleration. To apply as a Research Associate you must have a PhD (or equivalent) in
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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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. · A PhD (or be close to submission) in atomic physics or a closely related area. 2. Experience · Experience in conducting high quality academic research. · Demonstrable ability
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interested in PQC, FPGA/processor design, and aerospace cybersecurity. The candidate will join vibrant doctoral community, benefiting from world-class research facilities in the modern Dalton Building. Project
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of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard
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to perform. Knowledge, Qualifications, Skills and Experience Knowledge and Qualifications Essential: A1 Normally Scottish Credit and Qualification Framework level 12 (PhD) plus track record of emerging
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-power edge AI, microcontroller architectures, FPGA-based systems and VLSI, low-power and resource-constrained systems and analogue and mixed-signal systems. Publish high-quality outputs, pursue external