Phd student (d/f/m) Intelligent Process Monitoring and Digital Twinning

Develop a data-driven, real-time monitoring and diagnostic framework for cold-sprayed repair applications. You’ll research sensor detectability for process irregularities, design data acquisition and preprocessing pipelines for large datasets, and build ML/AI models to detect deviations. The work culminates in a digital twin architecture that links monitoring data to physical process behavior, followed by validation in an industrial setup and scientific publication.