Student assistant: Machine learning and FEM simulation for sheet metal forming

The Fraunhofer-Gesellschaft is one of the world's leading organizations for application-oriented research. 74 institutes develop pioneering technologies for our economy and society - more precisely: 30 000 people from technology, science, administration and IT. They know: Anyone who comes to Fraunhofer wants to and can make a difference. For themselves, for us and for the markets of today and tomorrow.

At the Fraunhofer IPT in Aachen, we work with more than 500 employees every day to make the production of the future more digital, more flexible and more sustainable. In the group »Integrated Production Machinery«, we specialize in optimizing manufacturing technologies for components used in energy conversion systems such as fuel cells and electrolyzers. With a focus on industrial applications, our expertise is concentrated on the fields of metallic bipolar plates for those applications.

As a student research assistant, you will support our team in developing automation methodology, machine learning models and data driven FEM simulation models for sheet metal forming processes. The position requires regular on-site presence at our institute in Aachen. The weekly working hours are at least 10 and at most 17 hours.

Be part of change

• CAE modelling and numerical simulation (e.g. sheet metal forming processes using ABAQUS, etc)
• Develop data-driven and machine learning-based models to improve the simulation and optimization of sheet metal forming processes
• Develop optimization methodologies for manufacturing process design and parameter identification
• Validation of the FEM and ML models through experimentation and documentation of results

What you contribute

• You are studying Computational Mechanics, Simulation Sciences, Mechanical Engineering or a comparable subject
• Initial experience in CAE modelling and FEM simulation (preferably Abaqus or Ansys) is required
• Prior knowledge of machine learning and experience in programming skills (preferably Python) is required
• Knowledge of Physics Informed Neural Networks (PINNs), PyTorch, TensorFlow is advantageous
• Experience with Fortran subroutines, automation scripting for FE software and CAD design (Solidworks, Catia) is advantageous
• Good language skills in English and/or German

What we offer

• Collaboration in innovative research projects and the ideal conditions to implement your knowledge from your studies and gain practical experience
• Flexible working to combine studies and job in the best possible way
• Opportunity to grow personally and benefit from our strong network in industry and research
• Would you like to deepen your knowledge further? We offer exciting topics for your thesis

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. Our tasks are diverse and adaptable - for applicants with disabilities, we work together to find solutions that best promote their abilities. Remuneration according to the general works agreement for employing assistant staff.

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.

Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.

For any further information on this position please contact:
Yogesh Borole M.Sc.
Research Assistant »Integrated Production Machinery«
+49 241 8904-469

Fraunhofer Institute for Production Technology IPT

www.ipt.fraunhofer.de