28 high-performance-quantum-computing-"https:"-"https:" Fellowship positions at UNIVERSITY OF SOUTHAMPTON
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We are seeking highly motivated post-doctoral researchers of high calibre to join our research program working in the area of electronic textiles. The role is suited to candidates from either
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on understanding the aerodynamics and aeroacoustics of overlapping propeller systems. This project will involve high-fidelity flow and noise measurements, combined with semi-analytical predictive models
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Coupled Plasma) torch design for high enthalpy thermal plasma flow. Developing/Validating computational models for a dual stage plasma torch. You should hold a PhD (or equivalent experience) in Plasma
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involving industry and academia, funded substantially by the UK Space Agency. This is part of the UK Space Agency National Space Innovation Programme just announced by UKSA. The overall project is to develop
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the spectrum of fibre laser development, from the development of new fibre core materials, to addressing limits imposed by instabilities, noise and nonlinearities, to high-speed beam and polarisation shaping
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researcher with a strong interest in optical fibre technology to work on the UK’s Engineering and Physical Sciences Research Council (EPSRC) funded Programme Grant, HiPPo (Smart Fibre optic High-power
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across the spectrum of fibre laser development, from the development of new fibre core materials, to addressing limits imposed by instabilities, noise and nonlinearities, to high-speed beam and
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biggest challenges. We are seeking a Research Fellow in Liquid Metal Artificial Enzymes to join the School of Electronics and Computer Science (ECS) at the University of Southampton. The successful
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. Deliver high-quality measurements of plant physiology and growth under defined environmental and acoustic conditions. Lead below-ground sampling and analyses, including root phenotyping/architecture
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by building new AI tools that can work alongside human analysts. Intelligence analysts are routinely required to make high-consequence, defensible assessments from vast, complex and uncertain datasets