ppinspire 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. /p pWe 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. /p h3Your Tasks /h3 ul liDevelop hybrid physical and data-driven models for condition assessment and lifetime prediction /li liDevelop and validate AI methods to identify degradation patterns in multisensor data /li liCreate pipelines for data acquisition, preprocessing, synchronization, annotation, and model training /li liBuild and validate sensor-based laboratory test setups and carry out experimental investigations /li liValidate the developed methods in an industrial environment /li /ul h3Required Experience /h3 ul liCH/EU/EFTA citizenship or a valid Swiss work permit /li liMaster’s degree (ETH, university) in mechanical engineering, mechatronics, robotics, computer science, or a related field /li liExperience in machine learning, computer vision, signal processing, or condition monitoring /li liStrong programming skills in Python and experience working with experimental data /li liStrong interest in combining mechanical modelling, experiments, and AI for safety-critical industrial systems /li /ul h3Required People Skills /h3 ul liSelf-motivated, structured, and eager to explore innovative technologies /li liStrong analytical mindset with hands‑on problem-solving abilities /li liComfortable working in interdisciplinary, industry-oriented teams /li liFluency in German and English is required /li /ul pThis 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. /p pFor technical questions, please contact Dr. Markus Maier, Head of Machine Concepts ( ). Please also visit our websites and /p /p #J-18808-Ljbffr
Project Engineer - AI-Based Condition Monitoring
