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Apply to Mercedes-Benz and find the area where you can develop your talents individually. You will be supported by visionary colleagues who share your pioneering spirit. Joining us means becoming part of a global team whose goal is to build the most desirable cars in the world. Together for excellence.

Number: MER00047M3 Mercedes-Benz AG is a pioneer in the automotive industry that continuously strives for technological breakthroughs. At the Sindelfingen site, our team “Future Digital Engineering” is engaged in the development of novel and innovative CAE methods for various applications in vehicle development. In our international team we work at the interface of research, innovation and industrial application. An important part of our work is the evaluation and integration of new technologies – including artificial intelligence, data-driven methods and modern simulation approaches – into existing CAE workflows to make future engineering processes more efficient, smarter and more efficient. We offer you the opportunity to participate in a future-oriented internship in novel methods for AI-based simulation. The focus is on connecting modern machine learning approaches with numerical simulation methods to make CAE processes more efficient and intelligent in the context of structural optimization. After the internship there is the possibility to continue your practical experience through a scientific deepening in the form of a final thesis. This gives you the chance to combine your theoretical knowledge with practical application and gain deeper insights into the interdisciplinary working of automotive development. Your tasks: Working on the development of data-driven CAE methods for predicting and approximation of simulation results

Processing, analysis and processing of large simulation data sets for machine learning applications

Development, training and evaluation of AI and deep learning-based surrogate models for finite element simulations

Development of Python and PyTorch-based workflows to automate data processing, model training and validation

Integration of modern AI methods into existing CAE and simulation processes

What we offer: Working with innovative research projects in Engineering AI and Deep Learning

Possibility to combine advanced methods from artificial intelligence and numerical simulation

Physics-Informed Machine Learning and Surrogate Modelling

Work with real simulation data from demanding engineering applications

Flexible work design and the possibility of introducing and implementing independent research ideas

Cooperation with experts from the fields of CAE, simulation, machine learning and software development

opportunity to develop knowledge in deep learning, PyTorch, FEM and data-driven modeling

The activity can start from 01. Start October 2026.

Studies in the field of master-comutational mechanics, simulation science, mechanical engineering, mathematics, computer science, artificial intelligence or a comparable degree programme

Very good knowledge of English; German knowledge is advantageous, but not absolutely necessary

Strong interest in deep learning, machine learning and the use of modern AI methods in the CAE environment

Basic knowledge of Python; Experiences with PyTorch or other deep learning frameworks are beneficial

Interest in developing AI models to predict physical processes and simulation results

Knowledge in the field of numerical simulation, finite element method or CAE applications is desirable

Experiences with LS-DYNA, HyperWorks, OptiStruct, Abaqus or comparable CAE tools are beneficial

Analytical way of thinking, self-initiative and a structured and independent way of working

Additional information: We are looking forward to your online application with CV, lettering, certificates, current enrollment certificate with a specification of the semester, if necessary. Compulsory internship certificate and proof of the regular study period. Please do not forget to mark your documents as ''relevant for this application' in the online form and note the maximum file size of 5 MB. Further information on the setting criteria can be found here. Disabled and equalized applicants are welcome! The severely disabled representative (sbv-sindelfingen@mercedes-benz.com) is happy to support you in the application process. People Solutions will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

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