Innovative materials play a key role in reducing carbon emissions and promoting sustainability. Consideration of performance should begin with the microstructural composition of the materials, since microstructural properties are the core of e.g. strength, corrosion resistance and lifetime. These properties can be permanently changed by manufacturing, loading and forming processes. A fundamental understanding of the physical mechanisms that characterize and determine the microstructure properties therefore enable the development of special-tailored materials that can exploit their full potential and can be precisely adapted to the process chain associated with an application. In this project, a digital material twin for metallic microstructures shall be created based on the evaluation of microstructures on different length scales. The goal is to further develop physically based material models, which preserve important physical phenomena and yield a continuum representation of microscale mechanisms based on statistical physics. With the incorporation of a meaningful homogenization of defect structures coupled with data-driven approaches, an advanced numerical formulation shall be established which bridge the modelling of plastic material behavior from the micro to the macro scale.

Candidates should hold an Master degree (or equivalent) in material science, mechanical engineering, numerical mathematics or physics with a solid theoretical background and interest in statistical physics and numerics. Additional knowledge of materials mechanics, finite elements, C/C++, modern data analysis/machine learning, or experience with other simulation methods is an asset.