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

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Bosch Group in Renningen seeks a candidate to advance machine learning based surrogate modeling for multiphysics simulations. You will help establish the scientific foundations for an ML framework, build surrogate models from validated EHL simulations, and design a protocol to accelerate lubricated component design. Requirements include a Master in Mechanical Engineering, Computational Engineering, Applied Mathematics or Physics, strong numerical methods, interest in ML, Python, and ideally knowledge of contact mechanics or EHL; fluent English, German a plus. Onsite, full-time. To apply, tailor your CV to show ML impact in engineering, include certificates, and prepare a research summary. For support contact Sarah Schneck or Cesar Pastor.

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: We grow together, we enjoy our work, and we inspire each other. Welcome to Bosch.

The Robert Bosch GmbH is looking forward to your application!

Job Description

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.

Qualifications

• Education: Master's degree in Mechanical Engineering, Computational Engineering, Applied Mathematics, Physics or comparable
• Experience and Know-how:
  • 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

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