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Field
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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model
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theories based on ab initio methods; exploring the role of nonlinearities in nuclear excitations; understanding nuclear reactions for fusion power technology; the properties of nuclei in stellar
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distribution, and kinetic parameters for step reactions. Revealing catalyst electronic structures and reaction energy barriers using ab initio calculations. Significance To date, over 70% of electricity in
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time-scales: ab initio methods for the description of reaction processes, for the determination of electrochemical stabilities and for the optimisation of force fields; molecular dynamics simulations
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of their detectors. As part of our group, you will play a vital role in developing an ab initio ('from first principles') nuclear framework to advance our understanding of neutrino-nucleus scattering for next
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approximate simulation methods with highly accurate reference DFT results. This will allow simulation of system sizes that are difficult to treat with fully ab initio theoretical approaches. Due to the project
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, or computer science Experience in modelling with ab initio electronic structure theory (at the level of DFT) in the field of catalysis, and preferably experience of electro-catalysis and VASP. Experience in AIMD
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, ab-initio material simulations, photonic engineering, laser physics, and ultrafast spectroscopy. By equipping them with both technical expertise and transferable skills in entrepreneurship and project
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theoretical uncertainties: develop Bayesian inference techniques to rigorously assess and quantify uncertainties in theoretical predictions Assess predictive power: explore the performance of ab initio nuclear
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the interplay between plasma turbulence and magnetic reconnection, as well as the relationship between instabilities and the dynamo process that sustains cosmic magnetization. By combining ab initio numerical