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models for atomistic systems (in chemistry or physics) is advantageous. The applicant should furthermore have a strong drive towards performing fundamental research; the ability and interest to work
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in a suitable software environment, with documented experience. Experience in applying or developing machine learning models for atomistic systems (in chemistry or physics) is advantageous
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of experienced researchers from different institutes at Forschungszentrum Jülich. As one of Europe’s largest and most multidisciplinary research centers, Jülich offers access to state-of-the-art infrastructure and
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including
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engineering, and materials science. The three-year PhD programme includes independent research work in a project involving different branches of science and engineering, and a well-structured scientific