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

In order to support the materials and processes department, Airbus Defence and Space is looking for a Phd student (d/f/m) in the field of Materials & Processes, Intelligent Process Monitoring and Digital Twinning for Cold-Sprayed Repair Applications You are looking for a PhD thesis and want to get to know the work in this area? Then apply now! We look forward to you supporting us in the Materials and Processes department as a Doktorand (d/f/m)! Location: Manching Start: 01.10.2026 / as soon as possible Duration: 36 months Your location Located about an hour’s drive north of Munich, Manching is an up-and-coming market town that offers a wide range of leisure and cultural activities. Here, you can enjoy the quality of life in the countryside while the pleasures of near-by cities are still within easy reach. Your benefits Attractive salary and work-life balance with an 35-hour week (flexitime). Traveling overseas or within Germany (team events) is possible after consultation and agreement from the department. International environment with the opportunity to network globally. Work with modern/diversified technologies. At Airbus, we see you as a valuable team member and you are not hired to brew coffee, instead you are in close contact with the interfaces and are part of our weekly team meetings. Opportunity to participate in the Generation Airbus Community to expand your own network. The repair of metallic components using cold spray technology is subject to increasingly stringent quality requirements, particularly in safety-critical applications. Ensuring process stability and part integrity demands advanced monitoring strategies. However, current quality control methods are typically performed offline (ex-situ), leading to delayed defect detection and increased scrap rates. Moreover, certain critical and non-acceptable defects remain undetectable with conventional approaches. To enable a true "Industry 4.0" implementation and establish cold spray as a qualified repair technology for high-integrity components, an integrated, real-time process monitoring system is essential. Such a system must continuously analyze production data and correlate it directly with component quality. This PhD project aims to develop a data-driven monitoring framework that combines sensor-based process data acquisition with advanced machine learning (ML) and artificial intelligence (AI) algorithms. The goal is to create an intelligent diagnostic tool capable of detecting process deviations in real time and feeding them into a digital twin architecture, enabling proactive optimization of process parameters in an industrial environment. Your tasks and responsibilities Conduct a comprehensive state-of-the-art review on detectability of process  irregularities and anomalies using sensor technologies (NDT & PM) Identify and integrate suitable sensor systems for capturing process-relevant signals correlated with deposition parameters Establish a robust data acquisition strategy and implement preprocessing pipelines for large-scale datasets (Big Data) Design and implement machine learning models and AI algorithms for automated recognition of process patterns and quality deviations Develop a digital twin framework by linking real-time monitoring data with physical process behavior Evaluate the reliability and robustness of the monitoring system under realistic conditions Implement the complete data pipeline into an industrial setup for validation and demonstration Preparation of conference papers and scientific publications in accordance with industrial guidelines and internal approval processes     Desired skills and qualifications Completed Master’s degree from a university in the field of Computer Science, Mechatronics, Physics, Data Science, or a related engineering or natural science discipline Strong background in data signal processing and machine learning (ML/AI) Proven experience with programming languages such as