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Thesis Autonomous Battery Manufacturing Optimization (f/m/x)
Job summary
A leading automotive manufacturer in Munich is offering a thesis opportunity in Autonomous Battery Manufacturing Optimization. You will contribute to developing innovative digital solutions for battery cell production while gaining valuable insights. Ideal candidates are computer science or engineering students with experience in Python, SQL, and machine learning. Join a team that values creativity and supports your professional growth, with some mobile work options available.
Benefits
Qualifications
- Study in computer science, data science, engineering, or a related field.
- Experience with Python and data handling using Pandas and NumPy.
- Knowledge of machine learning frameworks such as Scikit-learn, PyTorch, or TensorFlow.
- Familiarity with SQL databases and data extraction.
- Interest in agentic AI, RAG systems, and automation concepts.
- Experience with web technologies (React.js, Streamlit, Plotly) is a plus.
- Good English skills; German skills are an advantage.
Responsibilities
- Support extracting and processing data from various sources.
- Help transform and featurize data for predictive modeling.
- Contribute to developing surrogate models and implementing strategies for experiment selection.
- Assist in designing AI feedback loops for autonomous decision-making.
- Help create interactive dashboards for process monitoring.
Skills
Education
SOME IT WORKS. SOME CHANGES WHAT'S POSSIBLE.
SHARE YOUR PASSION.
More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. That’s why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table – and give you the opportunity to really show what you can do.
Our team at the BMW Group is dedicated to advancing battery cell manufacturing through innovative digitalization and AI-driven solutions. During your thesis, you will gain valuable insights into the development of autonomous optimization systems for complex production processes.
- You will support extracting and processing data from various sources such as SQL databases and Excel files.
- Furthermore, you help to transform and featurize data for predictive modeling and capture inter-stage interactions.
- In addition, you contribute to developing surrogate models and implementing active learning strategies for experiment selection.
- Moreover, you will assist in designing an agentic AI feedback loop using RAG systems for autonomous decision-making.
- You also help to create interactive dashboards for process monitoring and visualization using modern web technologies.
Please note that your thesis must be supervised by a university on your part.
- Studies in computer science, data science, engineering, or a related field.
- Experience with Python and data handling using Pandas and NumPy.
- Knowledge of machine learning frameworks such as Scikit-learn, PyTorch, or TensorFlow.
- Familiarity with SQL databases and data extraction.
- Interest in agentic AI, RAG systems, and automation concepts.
- Experience with web technologies such as React.js, Streamlit, or Plotly is a plus.
- Good English skills; German skills are an advantage.
Would you like to support the development of autonomous optimization systems for battery manufacturing? Then apply now!
- Mobile work.
- Apartments for students (subject to availability & only at the Munich location).
Do you have questions? Then submit your inquiry easily via our contact form . Your inquiry will be answered by phone or email afterwards.
We at the BMW Group place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experiences, and skills of the applicants. More about this here .
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.