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modeling, molecular interactions/energetics, and tools such as AlphaFold, Rosetta, or MD simulations. Solid programming skills (Python); familiarity with machine learning is a plus. For both profiles, we
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this position, you will employ molecular dynamics (MD) simulations to investigate the underlying atomistic mechanisms of LCI in steel grain boundaries and the inhibitory role of silicon. Your MD-based approach
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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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impacts of our missions and respond to the major scientific challenges of our times as captured in the new ESA science strategy https://esamultimedia.esa.int/docs/EarthObservation