Research Associate (Materials Science & AI) | Wissenschaftliche Mitarbeiterin/ Wissenschaftlicher Mitarbeiter (m/w/d)

Job Summary

This PhD research position, based at the University of Saarland within the Data-Driven Materials Design Group, contributes to the CircularSaar project, focusing on sustainable circular economy initiatives. The core responsibility involves using advanced machine learning (ML) and artificial intelligence (AI) methods to predict local material properties based on complex microstructure data. Day-to-day tasks include performing mechanical and micromechanical characterization, microscopically quantifying microstructures, and harmonizing diverse data sources (numerical, image, simulation). You will be responsible for testing and validating various ML approaches, including Deep Learning and Physics-Informed Neural Networks, to predict outcomes like local stress distributions or damage. A key attractive element is the opportunity to explore inverse ML models to identify optimal microstructures, leading to the development of novel materials that avoid critical elements. Success requires a completed university degree in Materials Science or Engineering, practical experience in metallography, and proficiency in Python and Matlab programming.