Master Thesis in Machine Learning Strategies for High Performance Contact Simulation

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. Join in and feel the difference.

The Robert Bosch GmbHis looking forward to your application!

Job Description

• Join our innovative team and collaborate with Bosch researchers to develop high-end simulation tools used in the design and development of critical components for market applications. 
• You will work in a team of researchers and gain a deep insight into our proprietary simulation software for contact dynamics (EHL). Your main focus will be on improving our models and methods to increase the speed, quality, and efficiency of the simulations using machine learning (ML) methods.
• Your area of ​​responsibility will include the development of algorithms and models to improve performance, scalability and efficiency.
• You will also analyze and interpret simulation output datasets to extract meaningful information for training your algorithms.
• Moreover, you will utilize machine learning techniques, ranging from Gaussian optimization to neural networks, to develop comprehensive design models based on extensive simulation data.
• Finally, you will test and demonstrate your improvements on real design challenges.

Qualifications

• Education: Master studies in the field of Engineering, Computer Science, Applied Mathematics or comparable degree in Science or Engineeringwith grades above average GPA
• Experience and Knowledge: proficiency in programming languages such as Python; strong background in AI; machine learning and optimization methods
• Personality and Working Practice: a person with a problem-solving mindset
• Enthusiasm for programming
• Languages: fluent in English 

Additional Information

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

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

#LI-DNI

Summary

• Type: Full-time
• Function: Research