Magnetron sputtering system (magnetron) by Inmodus licensed under


Research on applicability of machine learning in industry often focuses on specific applications (predictive maintenance, quality assurance, process control, etc.) and/or specific processes/process classes. One possible process class to investigate are vacuum deposition processes. These find application in a variety of promising industries and a wide range of products, such as semiconductors, solar cells, optical coatings, and many more.

Physical Vapor Deposition (PVD) by sigmaaldrich under CC BY-SA 4.0

Machine learning, deep learning, time series, vacuum deposition, physical vapor deposition (PVD), thin-film coatings



The goal of the thesis is to investigate the application of state-of-the art machine learning concepts and models on industrial time series data, esp. from vacuum deposition processes, for various use cases. Depending on the scope of the work (BT/PT/MT) and preferences, a possible assignment would be:

  • Technical familiarization and description of the state of the art in research related to multivariate industrial time series data for product quality prediction and process control
  • Identification and characterization of current attention mechanisms as well as evaluation of the potential applicability to industrial time series
  • Development of a machine learning/deep learning model based on the insights gained from the previous familiarization process
  • Evaluation of the model on the basis of various publicly available industrial time series data


Requirements profile and application information

  • Interest in machine learning in an industrial environment, ideally already initial experience
  • Highly motivated, perceptive and structured way of working as well as good communication skills
  • IT affinity and good knowledge of at least one high-level programming language (ideally Python) desirable
  • Start of work is possible immediately
  • Scope and content can be individually agreed depending on the type of thesis (BT/PT/MT) and personal preferences
  • Please send your application with CV and current subjects/grades by mail to the contact below


Research Sector:

Automation Technology

Type of thesis:

Bachelor Thesis, Master Thesis, Project Thesis


Energy Engineering, Informatics, IPEM, Engineering, Mechatronics, Industrial engineering


Artificial Intelligence and Machine Learning, Software Engineering and Deployment


Alexander Müller, M. Sc.

Department of Mechanical Engineering
Institute for Factory Automation and Production Systems (FAPS, Prof. Franke)