INTERNSHIP DETAILS

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

CompanyFraunhofer-Gesellschaft
LocationAachen
Work ModeOn Site
PostedMay 15, 2026
Internship Information
Core Responsibilities
Investigate various ML-based methods and their suitability as a surrogate for FEM. Implement a novel Graph Neural Network based algorithm to accelerate process stability calculation for machining processes.
Internship Type
full time
Company Size
285
Visa Sponsorship
No
Language
English
Working Hours
40 hours
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About The Company
Fraunhofer IGD is the international leading institute for applied research in visual computing. Visual computing is image- and model-based information technology and includes computer graphics and computer vision, as well as virtual and augmented reality. In simple terms, the Fraunhofer researchers in Darmstadt, Rostock, and Kiel are turning information into images and extracting information from images. In cooperation with its partners, technical solutions and market-relevant products are created. Prototypes and integrated solutions are developed in accordance with customized requirements. In doing so, Fraunhofer IGD places users at the forefront, providing them with technical solutions to facilitate computer work and make it more efficient. Owing to its numerous innovations, Fraunhofer IGD raises man-machine interaction to a new level. Man is able to work in a more result-oriented and effective way by means of the computer and visual computing developments.
About the Role

The »High-Performance Cutting« 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 

www.ipt.fraunhofer.de 

 

Requisition Number: 80874                Application Deadline:

 

Key Skills
Machine LearningPythonFinite Element MethodGraph Neural NetworksData AcquisitionProcess DesignProcess SimulationDocumentationDataset PreparationValidationCollaborationMechanical EngineeringIndustrial EngineeringComputer ScienceReal-Time DataPrototyping