X-ray Free Electron Lasers (XFELs) are complex, large-scale facilities. They heavily rely on ultrafast lasers used to generate electrons to be accelerated (photocathode lasers). The XFEL’s X-ray output is highly sensitive to the performance of such lasers. Therefore, a laser failure will stop the entire facility. This project aims to develop an advanced distributed anomaly detection framework leveraging machine learning and particularly exploiting the unique opportunity of having five identical photocathode laser systems, NEPAL (Next Generation Photocathode Laser), at three DESY facilities. To facilitate fault localization and preemptive interventions, we will create a framework that identifies nominal and problematic behavior of (sub-)components. These sub-component models are combined, based on the physical system structure, into a visual representation, i.e., Digital Shadow, of the laser system itself. Our approach uniquely combines learning causal structures and influences between subsystems with machine learning-based anomaly detection. At the end of the project, we will have an initial model for the NEPAL laser systems equipped with the most critical subsystems. This model is continuously updated at runtime and can be used by facility operators to analyze the system health and to plan system adjustments, such as preventive maintenance interactions. The model setup will consider the possibility of future expansions, enabling semi-automatic system adjustments and fault localization.

Requirements:

• Required Qualifications
• Academic Background
• Master’s degree in computer science, physics, electrical engineering, data science or comparable fields
• Technical / Methodical Skills
• basic knowledge of machine learning and statistical methods
• programming proficiency in Python
• hands-on experience with complex experimental set-ups and practical experience in interfacing physical hardware with software
• basic knowledge of causal inference (e.g., Bayesian Networks, Granger causality)
• strong interdisciplinary thinking between physics and computer science
• capacity for independent research and team collaboration
• demonstrated academic research track record (publications, presentations)
• Preferred Qualifications
• experience in time-series analysis and statistical methods (e.g., CUSUM, EWMA, ARIMA/SARIMA)
• solid understanding of machine learning, especially for small datasets (e.g., Isolation Forests, Autoencoders)
• familiarity with cyber-physical systems and system modeling
• experience with Digital Shadow modeling or process mining
• familiarity with MAPE-K architectures for adaptive systems
• knowledge of online learning and continuous model adaptation
• experience with lasers and ultrafast opics
• experience with large scale research infrastructure
• experience with data pipelines and control systems
• skills in data visualization and dashboard development
• motivation for interdisciplinary research in data science and physical systems
• willingness to work at two locations (DESY and HAW Hamburg)

Position:

• Deutsches Elektronen-Synchrotron DESY
• 75% EGR. 13 (TVöD) position for three years

At DESY, gender equality is an important aspect. To support work-life balance, we offer flexible working hours, variable part-time, job-sharing models, and participation in mobile work (up to 50%).

DESY promotes equal opportunities and diversity. The professional development of women is very important to us. Therefore, we strongly encourage women to apply for the vacant position.

Applications from severely disabled persons will be given preference if they are equally qualified (sbv.desy.de).