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. Welcome to Bosch.

The Robert Bosch GmbH is looking forward to your application!

Employment type: Limited
Working hours: Full-Time
Joblocation: Renningen

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.
• 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.

Your profile

• 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

This location offers

• Work-life balance: Flexible working in terms of time, place and working model.
• Health & Sport: Wide range of health and sports activities.
• Childcare: Intermediary service for childcare services.
• Employee discounts: Discounts for employees.
• Room for creativity: Space for creative work.
• In-house social counseling and care services: Social counselling and intermediary service for care services.

The recruitment contact or superior will be happy to provide information about the individual benefit plan.

Contact & Additional information

https://www.bosch-ai.com
www.bosch.com/research

Please submit all relevant documents (incl. curriculum vitae, certificates).

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need support during your application?
Sarah Schneck (Human Resources)
+49 9352 18 8527

Need further information about the job?
Cesar Pastor (Functional Department)
+49 711 811 43012

Work #LikeABosch starts here: Apply now!