This PhD position at the Technical University of Munich (TUM) offers an exciting opportunity to work on data-driven modeling and optimization of structural foams for high-voltage battery systems in electric vehicles. The project is conducted in close collaboration with BMW’s simulation and materials engineering teams, aligning with BMW’s passive safety and electrification strategy. The research aims to develop a multidisciplinary framework that integrates microstructure modeling, machine learning, and probabilistic simulation to connect manufacturing parameters, foam morphology, and mechanical performance.

The successful candidate will join the Data-driven Materials Modeling group under the supervision of Professor PS Koutsourelakis. The position is based in Garching, Germany, and is suitable for candidates with disabilities. Applicants should have a Master’s degree in Mechanical Engineering, Data Science, Materials Science, Physics, or a related field. Essential skills include strong experience with finite element methods and numerical simulations, a background in polymer or foam mechanics, microstructure modeling, and knowledge of optimization techniques. Proficiency in Python is required, and experience with MATLAB or other machine learning/data-science tools is advantageous. Familiarity with ML/AI methods such as regression, cross-validation, and hyperparameter tuning, as well as data-driven or surrogate modeling, is highly desirable. Excellent English language skills are mandatory, while German language skills are considered a plus.

Funding details are not explicitly provided, but the position is in partnership with BMW and TUM, offering a unique industry-academic research environment. The application process requires candidates to apply directly via the BMW web portal; applications sent by email will not be considered. For further information, candidates can consult the TUM and BMW job posting links provided in the announcement.

Key academic keywords for this position include data-driven modeling, structural foams, high-voltage battery systems, electric vehicles, mechanical engineering, materials science, finite element methods, polymer mechanics, foam mechanics, microstructure modeling, optimization, machine learning, Python, and MATLAB. The application deadline is October 30, 2025.