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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 the molten salt
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. Experience with molecular dynamics software such as LAMMS is desirable. Experience with molecular simulation software is beneficial. To apply please contact Dr Siperstein - flor.siperstein@manchester.ac.uk
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liquid environment. 1) Molecular Modeling - Study of interactions between MnO₂ and ionic liquids using Density Functional Theory (DFT) and Reactive Molecular Dynamics (ReaxFF). - Analysis of oxidation
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The aim of this project is to describe ion conduction and activation/inactivation processes by employing molecular dynamics and statistical mechanical methods. The expected outcome is an improved
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and biotic stresses. We utilize state-of-the-art LC-MS and RNA-seq techniques to address significant questions in tRNA biology, for example how post-transcriptional tRNA modification dynamics influences
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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, your application should include (PDF format): A letter of motivation (max. 1 page) A detailed curriculum vitae. Please include an overview of your experience with molecular dynamics, density-functional
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focuses on developing novel experimental techniques to visualize ultrafast molecular dynamics, probing mechanisms that break chemical bonds and form new ones in isolated molecules and clusters. The group is
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including pristine and defective Li2O, LiOH, LiH and their major surfaces. The simulations will be used to train machine-learned forced fields (MLFFs) to explore hydrogen diffusion using molecular-dynamic (MD
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dynamic changes in gene and protein expression as stem cells differentiate into mature blood cell types. This doctoral project focuses on developing computational methods to model cell development