Bachelor-/ Master Thesis: »Bayesian Optimization for Optimizing Laser Processes«

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research units throughout Germany and is a leading applied research organization. Around 32 000 employees work with an annual research budget of 3.4 billion euros.

At the Fraunhofer IPT in Aachen, we work with more than 530 employees every day to make the production of the future more digital, more flexible, and more sustainable. In the department “Production Quality”, we focus on data-driven optimization of production processes with the help of machine learning and artificial intelligence.

Laser processes enable highly precise and flexible material processing and are therefore crucial for innovative fields of technology such as semiconductor manufacturing and microsystems engineering and medical technology. The optimization of laser processes is becoming increasingly important, but also more complex, due to rising demands on performance, precision, quality, and sustainability. Bayesian optimization (BO) - a special machine learning approach - represents a promising alternative to classic methods of process optimization due to its adaptive decision making and data efficiency. In this thesis, a BO algorithm for the optimization of laser processes is to be developed, applied, and tested as part of a feasibility study. The aim is to determine the industrial maturity of BO and investigate how this can be increased in the future regarding its establishment in production engineering practice.

What you will do

You study mechanical engineering, automation technology, computer science, mathematics or a comparable subject
You are interested in the use of artificial intelligence in production technology
You are optimally already familiar with the theory and approaches of machine learning and would like to specialize in this field
Advanced programming skills in Python and related libraries (pandas, numpy, PyTorch) preferable
A high level of motivation, willingness to learn and independence
Good language skills in German and/or English

What you bring to the table

You study mechanical engineering, automation technology, computer science, mathematics or a comparable subject
You are interested in the use of artificial intelligence in production technology
You are optimally already familiar with the theory and approaches of machine learning and would like to specialize in this field
Advanced programming skills in Python and related libraries (pandas, numpy, PyTorch) preferable
A high level of motivation, willingness to learn and independence
Good language skills in German and/or English

What you can expect

Ideal conditions for practical experience alongside your studies
Professional supervision and collaboration in a dedicated team
A state-of-the-art machine park equipped with edge cloud systems and 5G infrastructure
Flexible working to combine study and job in the best possible way

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!

Questions according to this position will be answered by:

Lars Leyendecker M.Sc.
Group manager »Production Quality«
Phone: +49 241 8904-314

Fraunhofer Institute for Production Technology IPT

www.ipt.fraunhofer.de

Requisition Number: 75788 Application Deadline:

Job Segment: Mechanical Engineer, Computer Science, Medical Technology, Engineer, Semiconductor, Engineering, Technology, Healthcare, Science

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