Project Engineer - AI-Based Condition Monitoring

inspire AG; Zürich; Vor Ort; CHF 90.000 - 130.000; Vollzeit

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 is Switzerland’s leading competence center for product innovation and advanced manufacturing. As a strategic partner of ETH Zurich, our mission is to transfer knowledge and technology from academic research into the Swiss mechanical, electrical, and metal industries.

We are seeking a motivated engineer to join our team for a funded innovation project with a Swiss industrial partner. The focus is on sensor-based and model-based methods for real-time condition assessment and predictive maintenance of safety-critical mechanical systems. The work combines multisensor data, AI methods, physical models, experiments, and industrial validation.

Your Tasks

• 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

Required Experience

• CH/EU/EFTA citizenship or a valid Swiss work permit
• Master’s degree (ETH, university) in mechanical engineering, mechatronics, robotics, computer science, or a related field
• Experience in machine learning, computer vision, signal processing, or condition monitoring
• Strong programming skills in Python and experience working with experimental data
• Strong interest in combining mechanical modelling, experiments, and AI for safety-critical industrial systems

Required People Skills

• Self-motivated, structured, and eager to explore innovative technologies
• Strong analytical mindset with hands‑on problem-solving abilities
• Comfortable working in interdisciplinary, industry-oriented teams
• Fluency in German and English is required

This role offers a unique opportunity to work at the intersection of AI, sensor technology, mechanical modelling, and industrial experimentation. You will develop and validate advanced condition‑monitoring methods, from laboratory experiments and modelling to industrial application. The position combines academic depth with hands‑on engineering and offers excellent growth opportunities in applied research and development.

For technical questions, please contact Dr. Markus Maier, Head of Machine Concepts ([e-mail]). Please also visit our websites www.inspire.ch and https://mohr.ethz.ch/.