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Masterthesis: Development of data-driven models and correlation methods for raw part quality assurance

Quality assurance methods play a central role in aviation. Decisions that significantly impact flight safety are made based on regularly measured (material) data. However, not all desired data are readily available. Strength-related parameters in the component itself, for example, can only be estimated based on reference samples. The quality of the estimation is therefore significantly influenced by sample selection and the quality of prior information and assumptions.

With an increasing number of measured data points, it is not only possible to retrospectively review existing assumptions but also to continuously develop them. The synthesis of data-driven and physically motivated approaches promises to make a substantial contribution to prediction accuracy. This thesis aims to scientifically and practically develop such approaches, outlining their possibilities and limitations.


  • Familiarization with data-driven and physical material modeling
  • Development of suitable correlation methods to effectively and robustly estimate component properties based on existing tests
    • Implementation of robust statistical algorithms and mathematical error estimations
    • Validation with real datasets
    • Designing recommendations based on various scenarios
  • Independent evaluation, presentation, and documentation of results


  • Studies in (Technical) Mathematics, Mechanical Engineering, Physics, Data Science, or a comparable field
  • Solid expertise in probability theory and statistics
  • Programming experience with Python is advantageous
  • Basic knowledge in the field of materials science is advantageous
  • High structured and analytical thinking with a willingness to address complex issues
  • Initiative and solution-oriented work approach, along with independent work organization


  • Insights into the aviation industry and jobs with responsibility
  • Team spirit, e.g. through student network & events
  • Mentoring and individual support
  • Flexible working time models with mobile working
  • Canteen, café bar and sales shops
  • Sports cooperation with EGYM Wellpass & company sports groups 
  • Excellent connections thanks to commuter bus, parking garage, e-charging station 


Give your career a boost and send us your complete application (CV, Transcript of Records, High School Diploma).

We are looking forward to getting to know you!