PhD - Cross-Domain Hyperspectral Anomaly Detection for Manufacturing (f/m/div.)

The future of industrial manufacturing critically depends on the ability to detect even the smallest anomalies with precision and reliability. As a PhD candidate in our team, you will play a key role in redefining the boundaries of hyperspectral anomaly detection. You will develop robust AI systems that generalize across different materials and production sites, thereby helping to revolutionize quality assurance.

• In this role, you will combine cutting‑edge fundamental research with direct industrial application and actively shape the next generation of intelligent inspection solutions.
• You will develop and evaluate advanced machine learning methods for hyperspectral anomaly detection, leveraging self‑supervised representation learning as well as transfer and meta‑learning techniques, complemented by domain generalization approaches.
• Furthermore, you will analyze and process large volumes of hyperspectral data from real industrial applications as well as develop data‑efficient and scalable methods.
• As part of our team, you will work closely with internal and external partners to transfer research results into practice as well as ensure effective knowledge exchange.
• Last but not least, you will publish your research results in renowned scientific journals and present them at international conferences, actively contributing to the scientific community.