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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | about 2 months ago
of an accurate and generalisable density Equation of State model (EoS). This is especially challenging near the critical point and the Widom line, where there are large gradients in fluid properties. This project
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Computational Fluid Dynamics (CFD) to diagnose the air quality status of those spaces (presence of pollutants, ventilation, humidity) and to propose measures to improve it. Such measures might imply retrofitting
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. The successful candidate will be part of a team working towards advancing the modelling capabilities of geothermal systems and contribute to the following objectives: • Investigating fluid flow
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nanosheets, nanotubes, etc) or hybrid (e.g. boron carbon nitride). Similarly, while water is the most studied coolant liquid, realistic applications involve dielectric fluids (e.g. benzene, pentane). Molecular
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these robots utilise electronic, chemical, pressure, magnetic, or thermal mechanisms, with the current generation having significant drawbacks, including low energy efficiency, high operating voltage
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join a vibrant, supportive research community (around 20-25 people involved in fluids modelling research). Collaborate with the Leonardo Centre for Tribology: Work with top researchers on experimental
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hypothesis of the proposed research is by use of intelligent and integrated control of the input power electronics, fluid handling, and thermal control in a holistic approach, current efficiency and lifespan
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Current modelling and simulations require either generic assumptions to be made for fluid dynamic based modelling leading to inaccuracies between modelled and experimental data or, intense
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are invited for a full-time Research Assistantship for a period of six months. This project focuses on reducing aerofoil broadband noise, specifically turbulence–leading edge interaction noise and trailing edge
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This