General Information

Full Name Maximilian Benker
Year of Birth 1990
Languages English, German


  • 2018 - today
    Technical Univserity of Munich (TUM), Germany
    • Condition Monitoring of Machine Tool Feed Drives and Methods for the Estimation of Remaining Useful Life
  • 2015 - 2018
    M.Sc. Industrial Engineering and Management
    Technical Universtiy of Berlin, Germany
  • 2010 - 2014
    B.Sc. Industrial Engineering and Management
    Technical Universtiy of Berlin, Germany


  • 2023 - today
    Data Scientist Plant Service
    MTU Aero Engines AG, Munich, Germany
    • Development of condition-based maintenance solutions
  • 2018 - 2022
    Research associate
    Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Germany
    • Research in condition monitoring and estimation of remaining useful life for machine tool feed drives
    • Task and work package leader in the EU Horizon2020 research projects PreCoM and CogniPlant
    • Execution of data science consulting projects for industrial partners (predictive maintenance and predictive quality use cases)
    • Creation of and giving the lecture "Artificial Intelligence in Production Engineering" for TUM master students
    • Successful application of publicly funded research projects
  • 2021
    Visiting Researcher
    Chair of Intelligent Maintenance Systems, ETH Zurich, Switzerland
    • Scientific guest in Prof. Dr. Olga Fink's team
    • Application of deep learning models for condition monitoring of machine tool feed drives
  • 2015
    BMW Brilliance Automotive, Shenynag, China
    • Internship in R&D complete vehicle geometrical integration
    • Establishment of new R&D infrastructure with local suppliers
  • 2015
    Muller Dairy (UK) Ltd., Market Drayton, United Kingdom
    • Internship in operations and supply chain management
    • Determination of optimal safety stock levels for different SKUs

Academic Interests

  • Prognostics and Health Management
    • Condition monitoring of machine tool feed drives
    • Methods for the estimation of remaining useful life
  • Applied Machine Learning in Manufacturing
    • Predictive quality applications
    • Predictive maintenance applications
    • Data analytics in industrial applications