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

Do you have experience in Scripting?, Do you have a Master's degree?, * Education: Master's degree in Mechanical Engineering, Computational Engineering, Applied Mathematics, Physics or comparable, + in-depth knowledge of numerical methods

• a strong interest or background in machine learning
• experience or knowledge in contact mechanics and elastohydrodynamic lubrication (EHL) is desirable
• strong programming and scripting experience, preferably in Python

• Personality and Working Style: you have a high degree 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
• Languages: fluent in written and spoken English, good German language skills are an advantage

Shaping the future of engineering by redefining the boundaries between artificial intelligence and complex multiphysics simulations - that is your mission. Are you ready to make a crucial contribution to the development of groundbreaking design methods with your research? With us, you will not only create scientific knowledge but also lay the foundation for a new generation of efficient and reliable components in the industry. * 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.