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an outstanding PhD candidate in the field of computational electrocatalysis. The PhD project aims to use state-of-the-art quantum simulation techniques, Density-Functional-Theory (DFT), Ab Initio Molecular
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at the electrocatalytic interface under a magnetic field. The candidate will use AI-accelerated atomic-scale simulations, mainly in the framework of density functional theory, ab-initio molecular dynamics, and machine
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matrix of MSRs. We will benchmark the framework against experimental data. Our approach consists in coupling ab-initio quantum and classical molecular dynamics (MD) modelling techniques with x-ray
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machine learning models, probabilistic modeling or other bioinformatic tools Any of the following features are highly desirable, but not strictly required: Experience with applied analysis of proteomics
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vibrant interdisciplinary research environment. The position is part of a larger research project, "Bayesian neural networks for molecular discovery", and you will join an enthusiastic team working towards
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tools (webserver/package) Experience with machine learning Strong background in molecular/cellular biology Experience with analyzing large and complex datasets You must have a two-year master's degree
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, the section has a large project portfolio and currently employs 27 people with a background in molecular biology, immunology/vaccinology, diagnostics, virology, bacteriology, epidemiology and laboratory