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
What you bring to the table
What you can expect
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:
