PhD - Optimization Of Multi-Scale Simulation Parameters Using Agentic AI

The final μ-structure and properties of a component are strongly dependent on the prior manufacturing process
steps. The high number of parameters leads to a huge experimental parameter space to be investigated for new
product generations, μ-structure states and process chains and those influence on lifetime. As a result, it is timeconsuming to evaluate in product development different variants of materials and processes. Therefore, we strive for a virtual expert for metal components using an integrated multi-scale simulation chain with an agentic AI framework for reasoning tasks having in focus manufacturing processes and lifetime prediction.

• You develop concrete recommendations for design modifications - encompassing geometry, manufacturing processes, and material selection - to maximize our product performance.
• Furthermore, you optimize critical parameters and conduct complex simulations, strategically leveraging Large Language Models (LLMs) and your deep domain expertise.
• For our components, you establish and apply innovative workflows for diverse materials and manufacturing processes to generate precise lifetime predictions.
• As part of your tasks, you analyze simulation results in detail, draw informed conclusions, and derive concrete follow-up actions.
• Last but not least, you actively contribute to the further development of our reasoning models and integrate new findings from research and development.