PhD Candidate - 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 focuses on bridging the gap between artificial intelligence and classical engineering. As a researcher, you will develop a scientific foundation for a machine learning-based multiphysics framework, specifically utilizing surrogate models trained on validated elastohydrodynamic lubrication (EHL) simulations. Your day-to-day work involves creating computationally efficient, data-driven design protocols to accelerate the development of complex industrial components. By integrating AI into traditional engineering workflows, you will solve friction and wear challenges that current empirical models cannot handle. This role is ideal for candidates seeking to become experts in applying machine learning to complex engineering problems, preparing them for leadership roles in both academia and industry. You will work within the renowned Bosch Research environment, contributing to innovative solutions for robust and reliable tribological components while benefiting from a highly collaborative and technologically advanced corporate culture.