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High-precision laser-based material processing techniques such as laser structuring, laser metal deposition or laser beam welding are increasingly used as innovative manufacturing processes in automotive, tool making, aerospace or medical technology. These processes are characterized by a large number of distributed data sources, some of which are sampling at high frequencies.
In collaboration with the Fraunhofer Institute for Laser Technology ILT, data mining and machine learning methods are to be used to analyze this data and to develop predictive quality models. The insights gained will be fed back into the process development of the individual use cases.

Your tasks

  • Literature review on data mining and machine learning methods for laser materials processing.
  • Data preparation and in-depth data analysis to identify process characteristics
  • Development and evaluation of various data pre-processing steps and machine learning models (e.g., random forests, XGBoost, artificial neural networks) to predict the product quality
  • Evaluation, documentation, and regular communication of results to the process experts to increase the data quality and to drive the process development

Our requirements

  • You are studying mechanical engineering, industrial engineering, computer science or a comparable subject
  • You have a basic understanding of data science, machine learning and deep learning approaches
  • You have initial experience with the Python programming language and relevant machine learning libraries such as scikit-learn, pandas, tensorflow, keras, numpy etc.
  • A high degree of initiative, a structured way of working and motivation for the topic at hand
  • Good language skills in German and/or English

Your benefits

  • An ideal working environment for practical experience alongside your studies
  • Collaboration in a dedicated team of scientific researchers within the Fraunhofer Center for Networked, Adaptive Production (ICNAP)
  • Flexible working hours and the possibility to work remotely
  • Excellent equipment of machines and devices

We look forward to receiving your application
Lars Leyendecker
Fraunhofer-Institute for Production Technology IPT
Steinbachstraße 17, 52074 Aachen

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