Fraunhofer-Institut für Produktionstechnologie IPT

The Fraunhofer IPT uses machine learning (ML) models that can predict the quality of products.
Determining the optimal process parameters based on the ML models requires techniques for the
optimization. In this thesis, a literature review on optimizing production processes using ML as surrogate
models is to be carried out.

Your tasks

  • Familiarisation with the state of the art of optimization techniques
  • Literature review on techniques for parameter optimisation using surrogates (ML models)
  • Development of a catalogue of criteria for the evaluation of techniques
  • Evaluation and documentation of the results

Our requirements

  • You are studying engineering, computer science, operations research or a comparable subject
  • A high degree of initiative and team spirit
  • Very good language skills in English

Your benefits

  • Opportunity to contribute to relevant research in a pioneering project
  • Collaboration in a dedicated team consisting of engineers, data scientists and software developers
  • Direct and thorough supervision and the opportunity to publish the results

Notice: You can write your project work or thesis with us immediately and in direct cooperation with
the Chair of Metrology and Quality Management. According to the guidelines of the RWTH Aachen
University, the internship is preferably to be completed at a manufacturing company and can only take
place with us in exceptional cases.

We look forward to receiving your application
Hendrik Mende M. Sc.
Fraunhofer Institute for Production Technology IPT
Steinbachstraße 17, 52074 Aachen, Germany
hendrik.mende@ipt.fraunhofer.de

Um sich für diesen Job zu bewerben, sende deine Unterlagen per E-Mail an hendrik.mende@ipt.fraunhofer.de