Master Thesis: »Machine Learning (ML)-Based Methods as Surrogate for Finite Element Modelling«

The Fraunhofer-Gesellschaft is seeking a motivated student to implement a Graph Neural Network-based algorithm as a surrogate for Finite Element Modelling (FEM). The position offers professional supervision, a state-of-the-art machine park, and a collaborative team environment. The role focuses on accelerating process stability calculations for machining.

Requirements

• Studying mechanical engineering, industrial engineering, computer science or a comparable subject
• Good experience in Python
• Basic knowledge of the theory and methods in machine learning
• Good language skills in German and/or English

Benefits

• Professional supervision
• Collaboration
• State-of-the-art machine park
• Opportunity to contribute ideas
• Responsibility
• Focus on innovation