Master thesis + Quantum Machine Learning for Crashworthiness Optimization

vor 2 Wochen


Cologne, Deutschland Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) Vollzeit

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 tasksConduct focused literature research in the areas of quantum algorithms and surrogate modelingImplement quantum algorithms and develop quantum-enhanced surrogate modelsApply quantum machine learning models to benchmark functions and test their performanceCompare the performance of quantum models with classical approaches, analyzing strengths and limitationsYour profile Ongoing scientific university studies in mathematics, physics, computer science, engineering, or a related fieldExperience in optimization methods and techniquesStrong Python programming skillsProblem-solving skills and ability to work independentlyInterest in quantum computing and machine learningCuriosity and motivation to explore innovative computational methodsExperience with Gaussian Processes, GPyTorch, or PennyLane is a advantageous



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