Internship/Master thesis (f/m/x) - Quantum Machine Learning for Crashworthiness Optimization
At a glance
Junior Machine Learning Engineering role at DLR. Stuttgart.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Review research on quantum algorithms and surrogate modeling to guide your thesis work
- Build quantum algorithms and use them to create surrogate models
- Test quantum machine learning models on benchmark functions and assess their performance
- Compare quantum and classical methods, identifying where each performs well or falls short
What you bring
- You are studying mathematics, physics, computer science, engineering or a related subject at university
- You have experience with optimization methods and techniques
- You program confidently in Python
Who this fits
This thesis suits a student interested in quantum computing and machine learning who can investigate problems independently. It combines literature review, implementation and performance comparisons in quantum-enhanced surrogate modeling.
From the employer
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 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