Research Associate (Materials Science) | wiss. Mitarbeiterin bzw. Mitarbeiter (m/w/d)

Job Summary

This role involves developing a data-driven approach for the inverse design of microstructure-property relationships in austenitic steels, focusing on variations caused by recycling. Embedded within the DFG-funded research program "DaMic," the Research Associate will conduct comprehensive microstructure analysis using light and electron microscopy, and apply Machine Learning algorithms for automated image segmentation and statistical data preparation. Key responsibilities include developing workflows for simulation-supported (Calphad) microstructure prediction, validating these experimentally, and analyzing fatigue behavior at high load cycle numbers. This position offers the opportunity for further scientific qualification, typically leading to a doctorate (PhD), and requires close collaboration with project partners, making it ideal for highly motivated materials scientists seeking advanced research experience in sustainable structural metals.