In the simulation of the car body, you support the team in improving workflows and simulations. You contribute to the implementation of machine learning methods to develop innovative solutions.
What awaits you?
- You support the team in improving workflows and simulations of the car body, for example, by using neural networks.
- In this internship, you develop and evaluate codes for our workflows and investigate processes to implement various methods.
- Additionally, you assist with basic tasks such as data analysis and management presentations and conduct your own methodological investigations.
What should you bring along?
- Studies in computer sciences, software engineering, mechanical engineering or related fields with a focus on programming and machine learning.
- Extensive knowledge and experience in machine learning with Python (PyTorch and TensorFlow) and advantageous in Matlab, VBA, Java, and TCL.
- Experience in software engineering principles and related concepts (test-driven development, code reviews, Git branching, etc.).
- Familiarity with LINUX.
- Basic knowledge in acoustics, structural dynamics, stochastics, and fatigue is advantageous.
- Team and communication skills.
- Ability to speak and write in English fluently, German is advantageous.
Do you enjoy developing innovative solutions and supporting our team energetically? Then apply now!
What do we offer?
- Comprehensive mentoring & onboarding.
- Personal & professional development.
- Flexible working hours.
- Mobile work.
- Attractive & fair compensation.
- Apartments for students (subject to availability & only at the Munich location).
- And much more see bmw.jobs/whatweoffer.
Start date: from 10/01/2025
Duration: 12 months
Working hours: Part-time
Do you have questions? Then submit your inquiry easily via our contact form. Your inquiry will be answered subsequently by phone or email.
At BMW Group, we place great value on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants. More about this here.
