At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. 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.

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.