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particular focus on Small Modular Reactors and spent nuclear fuel management. The research will examine radiation- and high-temperature-resistant high-entropy alloys using ab initio, many-body, and Monte Carlo
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this change and explore cutting-edge methods in chemical, mechanical, and plasma processing of metal ores for a truly circular economy. Correlating experimental, ab initio, and multi-scale simulations, as
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force field parameterised on-the-fly on ab initio data. Additionally, quantum mechanical calculations will be used to characterize electronic structure, photocurrent generation and transfer in terms
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is to understand and optimize PPA properties using a simulation strategy that spans from the molecular scale (ab initio calculations) to continuum approaches, thereby accounting for the different
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developing an experimentally inspired theoretical approach to identify minimal atomic clusters involved in gas adsorption processes. As a practical outcome, adsorption energies will be calculated using ab
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of materials science, combining ab initio density-functional theory (DFT) calculations with novel ML methods. You will develop ML-assisted computational screening methods, building highly accurate models
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PhD Scholarship in High-level quantum chemistry calculations on key reactions in ammonia combustion.
For information on moving to Ireland please see www.euraxess.ie Detailed Project Description : Carry out high-level ab initio calculations for some key reactions in combustion chemistry model. Write and publish
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. Correlated experimental, ab initio and multi-scale techniques are central to our mission: Development and application of advanced simulation techniques to explore and identify the fundamental structures and
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position within the area of ab initio many-body theory with applications to excitons in complex 2D van der Waals materials. The position is funded via a EuroTech Alliance Stipend and involves collaborations
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. To do so, you will combine atomistic simulations (density functional theory and ab-initio molecular dynamics simulations) with new machine learning models to parameterize machine learning force fields