PhD - Machine Learning-based Surrogate Modeling for Computationally Efficient Multiphysics Simulation

Welcome to Bosch

Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe.

Your tasks

Hundreds of components are manufactured and continuously optimized across industries worldwide. Intense market competition demands more efficient design and simulation methods to support engineering innovation. Current design workflows struggle to handle the complexity of interacting parameters affecting friction and wear, relying heavily on empirical data or oversimplified models. In contact dynamics and elastohydrodynamic lubrication (EHL), the computational cost of accurate multiphysics simulations hinders their integration into practical design processes. Moreover, no comprehensive, experimentally validated framework exists to address these tribological design challenges systematically.

Your role will be to develop and establish the scientific foundations for a machine learning-based multiphysics framework, using surrogate models trained on validated EHL simulations. You will also create a novel, computationally efficient, data-driven design protocol for lubricated components. Furthermore, you will dramatically accelerate the design process for complex EHL problems, enabling the development of more robust, efficient, and reliable tribological components for critical industrial applications. You will be at the forefront of integrating AI into classical engineering design.

Your profile

Personality and Working Practice

You have a high level of motivation and scientific curiosity, work independently on complex issues, and always find your way to innovative solutions. You succeed in communicating your research results clearly and concisely and contributing constructively to a team. You organize your projects efficiently and keep an overview even with demanding schedules.