Master Thesis in The Field of Machine Learning and

vor 2 Monaten

Munich, Deutschland ZEISS Group Vollzeit

Your Role

We are looking for passionate and talented students who want to investigate physics-informed learning to increase the accuracy or speed of physics simulations at Zeiss. Physics-informed learning is a recent branch of machine learning that incorporates physical knowledge into machine learning, e.g., through choices of model architecture or special loss functions.

Integrated into a rapidly growing team of researchers at Corporate Research and Technology from whom you can learn, you will have the opportunity to tackle a variety of questions related to physics-informed learning by leveraging, e.g., graph neural networks, physics-informed neural networks, or neural differential equations.

You have- a bachelor’s degree in computer science, mathematics, physics, or related fields with an outstanding study record- interest in numerical simulation of (partial) differential equations- a good understanding of machine learning and deep learning- a solid programming background in Python and hands-on experience with machine learning frameworks like PyTorch or Tensorflow- a high motivation, creativity, and flexibility, as well as a structured and independent way of working- good communication and presentation skills

Your ZEISS Recruiting Team:
Mrs. Selina Riccio, Mrs. Trami Le

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