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Thesis: Adapting MLOps for AI/ML Integration in Maintenance

MLOps Engineer • Hybrid • Full-time Germany Aachen, Germany

Thesis opportunity focused on adapting MLOps approaches specifically to the maintenance use case. The student develops an approach enabling effective practical use of AI/ML in maintenance processes and derives recommendations for implementing tailored MLOps pipelines.

Stack

Responsibilities

  • Literature and market research on current MLOps trends and technologies and their application in maintenance
  • Analysis of maintenance-specific requirements for AI/ML models and their operation
  • Development of a tailored MLOps approach that facilitates integrating AI/ML into maintenance
  • Deriving recommendations and producing practice-oriented documentation based on the results

Requirements

  • Currently studying industrial engineering, mechanical engineering, computer science, or a related field
  • Excellent written and spoken German and English
  • Independent, committed, thorough, and goal-oriented way of working
  • Confident use of common MS Office applications

Soft skills

IndependenceThoroughnessGoal orientationCommitment

What we offer

  • Interesting and challenging tasks
  • Work on practice-relevant, cutting-edge research topics
  • Structured supervision with regular exchange (digital or on-site) and constructive feedback loops
  • Support for independent work and flexible time management
  • A motivated team and modern workplace at the Smart Logistics cluster

About the company

The company's service management department optimizes operation, maintenance, and upkeep processes for technical systems using modern technologies and innovative concepts.

Languages: Német: kiváló, Angol: kiváló
Education: Folyamatban lévő gazdasági mérnöki, gépészmérnöki vagy informatikai tanulmányok

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