Master Thesis: »Machine Learning (ML)-Based Methods as Surrogate for Finite Element Modelling«

The »« department develops technologies and application-oriented solutions for machining along the entire process chain - from process design and process simulation to real-time data acquisition during production, consulting, and prototype manufacturing. Graph neural networks provide an opportunity to operate on Mesh structured data utilized in Finite Element Method (FEM) simulations and offer time-saving benefits. We are looking for a dedicated and motivated student to assist us in implementing a novel Graph Neural Network based algorithm that can act as surrogate for FEM and accelerate process stability calculation for machining process.

What you will do

• Investigate various ML-based methods and their suitability as a surrogate for FEM
• Creation and preparation of dataset for appropriate use cases
• Implementation of selected ML model and validation of results
• Preparation and documentation of results

What you bring to the table

• You are studying mechanical engineering, industrial engineering, computer science or a comparable subject
• You have good experience in Python
• You have basic knowledge of the theory and methods in machine learning
• Good language skills in German and/or English

What you can expect

• Professional supervision and collaboration in a dedicated team
• You become part of the team from the very beginning, can contribute your ideas and take on tasks on your own responsibility
• A state-of-the-art machine park equipped with edge cloud systems and 5G infrastructure

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Interested? Apply online now. We look forward to getting to know you!

For any further information on this position please contact:
Aakash Singh M.Sc.
Research Assistant »High Performance Cutting«
Phone: +49 241 8904- 587

Fraunhofer Institute for Production Technology IPT

Requisition Number: 80874 Application Deadline: