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The simulation of crash scenarios by means of the Explicit Finite-Element-Method is one of the most important cornerstones in the vehicle development process. To achieve the vision of a prototype-free development, the CAE-tools must be continuously improved. This master thesis contributes to this goal by using modern machine learning approaches to develop efficient surrogate models for the local failure of connection techniques and other critical points in the vehicle structure.


  • Getting started with crash simulation
  • Analysis of existing model approaches
  • Test and evaluation of different machine learning approaches (LOLIMOT, ANFIS, SVM etc.) on existing data
  • Implementation of suitable approaches in the L2-Failure Assessment Framework developed at Virtual Vehicle Research Center


  • Master studies in mechanical engineering, physics, mathematics, electrical engineering or similar.
  • Knowledge of Python, Matlab, Fortran or C++
  • Good knowledge of statistics and data analysis
  • Passion for simulation and programming


  • Collaboration and contribution in an engaged, dynamic team
  • Interesting work in an international research center
  • Paid Thesis
  • Mentoring program
  • Diverse sports and health activities
  • Corporate events

For technical questions, please contact

Karlheinz Kunter