Share this Job

Work at the forefront of automotive innovation with ZF,
one of the world’s leading automotive suppliers.

Internship/ Master Thesis: Physics enhanced machine learning for vibration prediction


Antwerpen, Antwerp, BE, 2600

Req ID 26648

We are looking for students (m/f/d) for ZF Wind Power in the noise and vibration research and development team at our location in Antwerp (Belgium). You will study the physics enhanced machine learning for vibration prediction of wind turbine gearboxes.


Your tasks:

  • Simulation of the dynamic behavior of gearboxes using data driven (machine learning) and physics based (multi-body) models
  • Research on new techniques to enhance data driven models with physical knowledge or vice versa
  • Evaluation of developed approaches with respect to their robustness and potential for generalization
  • Implementation of Gearbox Digital Twin functionality and automated calculation tools for gearboxes


Your profile:

  • Pursuing a degree in Mechanical Engineering, Data Science, or similar field of study
  • Programming skills (Python), first experience with machine learning
  • Basic knowledge of drive train and gear technology as well as first experiences with multi-body modelling are an advantage
  • Team spirit, analytical skills, quick comprehension, solution-oriented and independent working style
  • Excellent communication skills in English


Be part of our ZF team and apply now!


Helena Paul

(+49) 7541 77969121

Our Commitment to Diversity

ZF is an Equal Opportunity and Affirmative Action Employer and is committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job-related reasons regardless of an applicant's race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, marital status, genetic information, protected veteran status, or any other status protected by law.

Job Segment: R&D Engineer, Mechanical Engineer, Engineer, Intern, Engineering, Automotive, Entry Level