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

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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.

Responsibilities

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.; Last but not least you will also become an expert in applying machine learning to complex engineering challenges, a skill set that will make you exceptionally valuable for leading roles in both industry and academia.

Requirements

Education: Master's degree in Mechanical Engineering, Computational Engineering, Applied Mathematics, Physics or comparable; Experience and Knowledge:; in-depth knowledge of numerical methods; strong programming and scripting experience, preferably in Python; experience or knowledge in contact mechanics and elastohydrodynamic lubrication (EHL) is desirable; 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; Enthusiasm: a strong interest or background in machine learning; Languages: fluent in written and spoken English, good German language skills are an advantage