Master Thesis GPT and Quantum Machine Learning (f/m/x)

BMW Group; München; Hybrid; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

An international automotive company in Munich is offering a Master Thesis opportunity in GPT and Quantum Machine Learning. Students in computer science, physics, or related fields are invited to support innovative research by designing frameworks, testing workflows, and evaluating performance across quantum backends. Strong programming skills in Python and experience with deep learning frameworks are required. This position offers mobile work and student apartments based on availability.

Leistungen

Mobile work

Apartments for students

Qualifikationen

• Strong programming skills in Python.
• Experience with deep learning frameworks such as PyTorch or JAX.
• Background or strong interest in quantum machine learning.

Aufgaben

• Support designing and implementing a framework for quantum circuit generation.
• Help develop, test, and benchmark prototype workflows for quantum machine learning.
• Assist with evaluating performance across quantum backends and simulation environments.

Kenntnisse

Strong programming skills in Python

Experience with deep learning frameworks

Interest in quantum machine learning

Ausbildung

Studies in computer science, physics, mathematics, engineering or a related field

Tools

PyTorch

JAX

PennyLane

Qiskit

Jobbeschreibung

Master Thesis GPT and Quantum Machine Learning (f/m/x)

IT Innovation and Research Munich 27.10.2025

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 explores digital innovation across all technology domains, from cloud and AI to quantum computing. As part of the Innovation, Emerging Technologies department, you will support our collaboration with leading industrial and academic partners to advance next-generation computing paradigms. This thesis is conducted within the QUTAC consortium, working closely with industry experts and BMW research teams.

What awaits you?

• You will support designing and implementing a framework that connects LLM outputs to quantum circuit generation.
• Furthermore, you help to develop, test, and benchmark prototype workflows for quantum machine learning applications.
• In addition, you contribute to evaluating performance and scalability across quantum backends and simulation environments.
• Moreover, you will assist in close collaboration with QUTAC industry experts and BMW research teams.
• You will help to document results and derive academic insights suitable for publication.

Please note that your thesis must be supervised by a university on your part.

What should you bring along?

• Studies in computer science, physics, mathematics, engineering or a related field.
• Strong programming skills in Python.
• Experience with deep learning frameworks such as PyTorch or JAX and quantum libraries like PennyLane or Qiskit.
• Background or strong interest in quantum machine learning or hybrid AI–quantum methods.
• Analytical mindset, curiosity for emerging technologies, and ability to support research activities.
• Excellent command of English (spoken and written).

Would you like to support our research on the interface of AI and quantum computing? Then apply now!

What do we offer?

• 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.