PhD - Cross-Domain Hyperspectral Anomaly Detection for Manufacturing (f/m/div.)
Arbeitsmodus
Hybrid
Arbeitsbereiche
Forschung, Voraus und Technologieentwicklung
Einstieg als
Absolvent*in
Startdatum
Nach Vereinbarung
Arbeitszeit
Vollzeit
Abteilung
Corporate
Aufgaben
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.
Profil - Education: completed Master’s degree in computer science, machine learning, artificial intelligence, or a related field with excellent academic performance
- Experience and Knowledge:
- solid experience with machine learning methods, particularly in the field of deep learning
- very strong programming skills in Python
- experience with at least one deep learning framework (e.g., PyTorch or JAX)
- strong background in computer vision and probabilistic modeling
- knowledge of representation learning, self‑supervised learning, or transfer learning
- interest in digital signal processing, physics, optics, photonics, or materials science is a plus
- Personality and Working Practice: you analyze complex research questions with strong analytical skills and develop innovative solutions; you work independently in a structured and goal‑oriented manner, clearly communicate your results, and take responsibility for your research; you also collaborate effectively with industrial partners and demonstrate high intrinsic motivation for high‑quality research in an industrial environment
- Enthusiasm: you have a strong interest in machine learning and computer vision for industrial applications and are passionate about solving challenging real‑world problems through research
- Languages: Very good English skills required; German is a plus
- Education: completed Master’s degree in computer science, machine learning, artificial intelligence, or a related field with excellent academic performance
- Experience and Knowledge:
- solid experience with machine learning methods, particularly in the field of deep learning
- very strong programming skills in Python
- experience with at least one deep learning framework (e.g., PyTorch or JAX)
- strong background in computer vision and probabilistic modeling
- knowledge of representation learning, self‑supervised learning, or transfer learning
- interest in digital signal processing, physics, optics, photonics, or materials science is a plus
- Personality and Working Practice: you analyze complex research questions with strong analytical skills and develop innovative solutions; you work independently in a structured and goal‑oriented manner, clearly communicate your results, and take responsibility for your research; you also collaborate effectively with industrial partners and demonstrate high intrinsic motivation for high‑quality research in an industrial environment
- Enthusiasm: you have a strong interest in machine learning and computer vision for industrial applications and are passionate about solving challenging real‑world problems through research
- Languages: Very good English skills required; German is a plus
