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Salary: Research Assistant: £32,546 - £33,482 per annum Research Associte: £35,116 - £38,249 per annum Newcastle University is a great place to work, with excellent benefits . We have a generous
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desorption mechanisms of various FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property
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Kyropoulou and will work in areas related to (Theoretical) Computer Science, Algorithmic Game Theory, and/or Fair Division with potential applications in Blockchain in the context of the EPSRC funded research
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of polycrystalline metals. The post holder will be located in Central Cambridge, Cambridgeshire, UK. The key responsibilities and duties include setting up the framework; validating the results using theory and
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(developed by B.J. Evans, O. Hassan and K. Morgan). This solver directly solves the Boltzmann-BGK model equation for the velocity distribution function, which is a fundamental quantity in rarefied gas
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. The successful applicants are expected to work with Dr. Yihan Zhang on “Precise Asymptotics in High-Dimensional Statistics using Random Matrix Theory and Statistical Physics ”. Applicants with mathematics
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that centres liveability and lived/living experience. More information about our project can be found here https://blogs.ed.ac.uk/discovering-liveability/ . The post requires in-person ethnographic work in an
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, the link arises because APE is created at high latitudes by strong surface cooling, cause APE anomalies to propagate along the western boundary. To test the new theory, the aim will be to diagnose the AMOC
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simulation regimes by harnessing and advancing the latest developments in AI Machine Learning. This studentship is a continuation of prior work that is looking at using new cutting-edge deep learning models
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning