Thesis Using Geometrical Deep Learning for Simulation

Thesis Using Geometrical Deep Learning for Simulation

Full-time

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 Power Tools GmbH is looking forward to your application!

• During your thesis, you will familiarize yourself with the software landscape for simulation at Bosch Power Tools.
• You will conduct a literature review on the topic of Geometrical Deep Learning from an application-oriented perspective.
• Furthermore, you will work with and evaluate a commercially available tool.
• Your job includes the integration and implementation of existing simulation models and data. You will train AI models with already existing simulation results and evaluate the quality and plausibility of the obtained AI model results.
• Finally, you will derive recommendations for the use of these models in the context of Power Tool simulations.

Education & Experience

• Education: studies in the field of Mechanical Engineering, Mechatronics, Aerospace Engineering, Computational Engineering, or comparable with a focus on mechanics and dynamic simulation
• Experience and Knowledge: confident use of MS Office, initial knowledge of FEM simulations (ideally Abaqus and HyperWorks); fundamentals of Data Science
• Personality and Working Practice: you excel at proactively and responsibly driving tasks forward, leveraging your strong analytical and communication skills to work effectively, both independently and as a collaborative team member
• Work Routine: your on-site presence is required
• Languages: fluent in English and German

Start: according to prior agreement
Duration: 4 - 6 months

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.

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