Key Skills

Job Description

Job Description Background Aircraft engines and battery systems operate under changing and demanding conditions, which lead to complex degradation over time. Factors such as operating conditions, mission profiles, environmental effects, and component aging all influence the performance of these systems. Recent advances in machine learning have enabled powerful methods for health monitoring and prognostics using measurement sensor data. In particular, latent dynamical models and probabilistic deep learning methods have shown strong potential for modelling complex temporal behaviour. However, many existing approaches mainly focus on prediction accuracy, while offering limited interpretability. In safety-critical aerospace applications, this can result in overconfident predictions, unrealistic degradation behaviour, and reduced trustworthiness for maintenance decision-making. A promising direction is the integration of reliability engineering principles and degradation physics into latent dynamical learning frameworks. By constraining or guiding the evolution of latent health representations using reliability-informed stochastic processes, degradation-aware priors, or physically meaningful dynamics, it becomes possible to improve model interpretability, uncertainty quantification, robustness, and extrapolation capabilities under changing operational conditions. Despite its strong potential, this integration remains relatively underexplored in current AI-based prognostics research. This project aims to bridge reliability engineering, stochastic degradation modelling, and modern AI-based prognostics. The proposed framework will investigate learned latent health representations whose evolution follows reliability-aware stochastic dynamics. References https://neurips.cc/virtual/2022/59892 T. Christen and F. Macedo, "Theory of Fokker–Planck Equations for Reliability and Remaining Useful Lifetime Prognostics," in IEEE Transactions on Reliability , vol. 73, no. 1, pp. 344-356, March 2024, doi: 10.1109/TR.2023.3311230. Pierre Dersin, Roberto Rocchetta , “ Analysis of RUL dynamics and uncertainty via time transformation” Reliability Engineering & System Safety, Volume 266, Part B, 111730 , February 2026 Dersin, P., Goglio, D., Bajarunas, K., & Arias Chao, M. (2025). Analytical Health Indices: Towards Reliability-Informed Deep Learning for PHM . International Journal of Prognostics and Health Management, 16(2), Article 4262. Assignment Reviewing recent research on latent dynamical models, deep learning,-based uncertainty quantification, and AI for prognostics and health management (PHM). Developing AI models capable of learning latent degradation representations, modelling degradation evolution, and estimating predictive uncertainty. Investigating reliability-aware constraints in latent space, such as realistic degradation behaviour, uncertainty propagation, and reliability boundary conditions. Training and evaluating models using aircraft propulsion datasets (e.g., N-CMAPSS), battery degradation datasets, or industrial condition monitoring data. Studying model robustness under changing operating conditions, unseen scenarios, and limited failure data. Analysing model behaviour and trustworthiness, particularly in ambiguous or rare failure scenarios, and exploring techniques to reduce false positives. Documenting the project outcomes, and if feasible, contributing to research publications or internal white papers relevant to predictive maintenance. Results The Final Outcomes Of This Assignment Will Be A robust, uncertainty-aware anomaly detection pipeline for aircraft health monitoring, built with real-world relevance and deployability in mind. Optionally: a conference and/or journal publication. Duration For an MSc thesis, the standard duration of a thesis at your faculty applies (typically 9 months). For an internship, the duration is 6 months. Profile You are an MSc student in Aerospace Engineering, Computer Science, Artificial Intelligence or a similar master. You have experience with programming in Python and packages for machine learning, for instance PyTorch. You have completed a Machine Learning or AI course (e.g., DSAIT4005, DSAIT4115, AE2224-II). You have an affinity with maintenance, reliability engineering or PHM, and are motivated to explore the application of a new reliability-informed paradigm to aerospace systems. What We Offer A challenging graduation project/internship in a high-tech result orientated work environment Weekly supervision and availability of the technical staff for support An internship allowance Working in an actual R&D project as part of the team Internship results to be used in the current and future projects About NLR Royal NLR has been the ambitious research organisation with the will to keep innovating for over 100 years. With that drive, we make the world of transportation safer, more sustainable, more efficient and more effective. We are on the threshold of breakthrough innovations. Plans and ideas start to move when these are fed with the right energy. Over 800 driven professionals work on research and innovation, from aircraft engineers to psychologists and from mathematicians to application experts. Our colleagues are happy to tell you what it's like to work at NLR. This assignment will be managed by the Avionics Systems and Maintenance Engineering (ASAM) department. Interested? Submit your application via the NLR portal, together with your (i) Motivation letter and (ii) CV and we will contact you as soon as possible. For more information about the assignment contact Daniel Friesen ( [email protected] ) or Lisandro Jimenez ( [email protected] ).

Core Responsibilities

Review research and develop AI models that learn latent degradation representations, model their evolution, and estimate predictive uncertainty. Evaluate reliability-aware models on propulsion, battery, or industrial monitoring datasets, assess robustness and trustworthiness, and document findings for potential publications or internal reports.

Requirements

Applicants should be MSc students in Aerospace Engineering, Computer Science, Artificial Intelligence, or a similar field, with Python and machine-learning package experience such as PyTorch. They should have completed a machine-learning or AI course and be interested in maintenance, reliability engineering, or prognostics and health management.

Benefits

• Internship Allowance
• Weekly Supervision
• Technical Staff Support
• Participation in an Active R&D Project