PhD Student - Machine Learning-based Surrogate Modeling for Multiphysics Simulations | PhD - Machine Learning-based Surrogate Modeling for Computationally Efficient Multiphysics Simula...

Job Summary

This PhD position at Bosch Research focuses on bridging the gap between artificial intelligence and complex multiphysics simulations. As a researcher, you will develop the scientific foundations for a machine learning-based framework using surrogate models trained on validated Elastohydrodynamic Lubrication (EHL) simulations. Your daily work involves creating a novel, data-driven design protocol for lubricated components to dramatically accelerate design processes for industrial applications. You will be responsible for integrating AI into classical engineering design, ensuring the development of robust and reliable tribological components. This role is ideal for candidates who want to lead the way in AI-driven engineering. The position offers a unique opportunity to gain expertise in applying machine learning to complex engineering challenges, positioning you for high-level roles in both industry and academia. You will work within a highly innovative environment at Bosch, contributing to groundbreaking research that impacts real-world industrial components.