Student assistant: Physics-based Machine Learning for Process Optimization in Machining Domain

The Fraunhofer-Gesellschaft (www.fraunhofer.com) is one of the world’s leading organizations for application-oriented research. 75 institutes develop pioneering technologies for our economy and society – more precisely: 32 000 people from technology, science, administration and IT.
At the Fraunhofer Institute for Production Technology IPT in Aachen, we are creating the production of the future with more than 450 employees – digital, sustainable and resilient. The »High-Performance Cutting« department develops technologies and application-oriented solutions for machining along the entire process chain – from process design to real-time data acquisition for generating a digital twin during production to consulting and prototype production.
As a student assistant, you will support our team in advancing Physics-based machine learning approaches for detecting surface-quality-critical vibrations from sensor data, enabling data-driven optimization of machining processes. You will also implement ML algorithms for additional use cases, accelerating workflows and enhancing the accuracy of the digital twin platform developed at Fraunhofer IPT.
The job requires regular attendance at our institute in Aachen. The weekly working hours are at least 12 hours.

Be part of change

• Develop and implement machine learning algorithms to model and analyze dynamic phenomena in milling processes
• Explore and apply Physics-Informed / Physics-Guided Machine Learning approaches
• Design and develop cloud-based microservices for industrial manufacturing applications

What you contribute

• You are studying Mechanical Engineering, Computer Science, Mechatronics or a comparable subject
• Solid background in programming (e.g., Python or C++) is essential
• Experience with machining processes or ML approaches are advantageous
• A high degree of motivation, initiative, independence
• Good language skills in German and/or English

What we offer

• Collaboration in innovative research projects and the chance to implement your knowledge from your studies in practice
• A state-of-the-art machine park equipped with edge cloud systems and 5G infrastructure
• Flexible working to combine studies and job in the best possible way
• The opportunity to write your practice-oriented thesis with us

Ready for a change? Then apply now and make a difference!

https://jobs.fraunhofer.de/job-invite//

For any further information on this position please contact:
Aakash Singh M.Sc.
Research assistant, High Performance Cutting Department
Phone: +49 241 8904-373 Requisition Number: 83507