AI-Driven Predictive Maintenance Engineer

inspire AG

Zürich

Vor Ort

CHF 90.000 - 130.000

Vollzeit

14 Tage+

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Zusammenfassung

inspire AG, Switzerland’s leading competence center for product innovation and advanced manufacturing, seeks an engineer for a funded innovation project with a Swiss industrial partner. The role focuses on sensor-based and model-based methods for real-time condition assessment and predictive maintenance of safety-critical mechanical systems.

You will develop hybrid models, implement AI to detect degradation in multisensor data, and build data pipelines for acquisition, preprocessing, and

Qualifikationen

  • Master’s degree in a mechanical/ME related field (ETH/university).
  • Experience in ML, computer vision, signal processing, or condition monitoring.
  • Strong Python programming skills and experience with experimental data.

Aufgaben

  • Develop hybrid physical and data-driven models for condition assessment and lifetime prediction.
  • Develop and validate AI methods to identify degradation patterns in multisensor data.
  • Create pipelines for data acquisition, preprocessing, synchronization, annotation, and model training.
  • Build and validate sensor-based laboratory test setups and carry out experimental investigations.
  • Validate the developed methods in an industrial environment.

Kenntnisse

Machine learning
Computer vision
Signal processing
Python programming
AI for condition monitoring
Analytical mindset
Interdisciplinary teamwork

Ausbildung

Master’s degree in mechanical engineering or related field

Tools

Python programming
Experimental data analysis

Jobbeschreibung

inspire AG, Switzerland’s leading competence center for product innovation and advanced manufacturing, seeks an engineer for a funded innovation project with a Swiss industrial partner. The role focuses on sensor-based and model-based methods for real-time condition assessment and predictive maintenance of safety-critical mechanical systems.

You will develop hybrid models, implement AI to detect degradation in multisensor data, and build data pipelines for acquisition, preprocessing, and

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