Internship/Master thesis (f/m/x) Quantum Machine Learning for Crashworthiness Optimization

Job Description

Req ID:

6446

Place of work:

Stuttgart

Starting date:

immediately

Career level:

Internship, Student research project and final thesis, Student employment

Type of employment:

Part time

Duration of contract:

4 to 6 months

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund).

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 12,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

The Institute of Vehicle Concepts (FK) of the German Aerospace Centre (DLR) is internationally recognized for the design of future road and rail vehicles that enable climate and environmentally friendly mobility while being affordable and user-friendly at the same time.

We research and demonstrate the required key technologies and maintain close cooperation with other scientific institutions as well as industrial and political bodies.

What to expect

In crashworthiness optimization, we aim to design vehicles that are robust, safe, and lightweight. Evaluating these designs is computationally expensive, so we use surrogate models to approximate costly simulations. However, mathematical bottlenecks within the surrogate modeling process can limit efficiency and accuracy. In this Master’s thesis, you will explore how quantum algorithms can accelerate and enhance surrogate modeling, gaining hands-on experience with quantum machine learning methods, engaging with the latest literature, and contributing to cutting-edge research at the intersection of quantum computing and predictive modeling.

Your tasks

• Conduct focused literature research in the areas of quantum algorithms and surrogate modeling

• Implement quantum algorithms and develop quantum-enhanced surrogate models

• Apply quantum machine learning models to benchmark functions and test their performance

• Compare the performance of quantum models with classical approaches, analyzing strengths and limitations

Your profile

• Ongoing scientific university studies in mathematics, physics, computer science, engineering, or a related field

• Experience in optimization methods and techniques

• Strong Python programming skills

• Problem-solving skills and ability to work independently

• Interest in quantum computing and machine learning

• Curiosity and motivation to explore innovative computational methods

• Experience with Gaussian Processes, GPyTorch, or PennyLane is a advantageous

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 6446) please contact:

Mr. Kerem BÜKRÜ Phone: +49 711 6862 515