Master thesis + Quantum Machine Learning for Crashworthiness Optimization (f/m/d)

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