Thesis Worker 30 hp - Integrating Wear and Sensor information

Job description** The thesis will investigate probabilistic fusion of wear-based and sensor-based models for Remaining Useful Life prediction. This work will include: 1. Reproduce and validate the proposed framework: Implement and evaluate the approach presented in \[1\], including the survival-based aging model, neural-network-based RUL model, and their probabilistic combination. 2. Explore alternative modelling approaches: Investigate alternatives to the Weibull survival model and/or neural-network architecture used in \[1\]. Evaluate how different modelling assumptions influence RUL prediction and uncertainty. 3. Evaluate generalisation across datasets: Test the framework on additional datasets and operating conditions to study its robustness and ability to generalise beyond the original experimental setting. 4. Investigate uncertainty quantification for RUL prediction: Compare different approaches for estimating predictive uncertainty and study their effect on the resulting RUL distributions, prediction intervals, calibration and accuracy. If time permits, investigate extensions that relax the proxy assumption used in the data-generating formulation in \[1\], with the aim of developing a more general relationship between wear, latent component health, sensor observations, and failure. The thesis provides an opportunity to work at the intersection of machine learning, survival analysis, uncertainty quantification and predictive maintenance, with both methodological research questions and applications to real-world industrial systems. **Reference** \[1\] Srinivasan, Abhishek, et al. "Integrating Survival-Based Aging Models with Data-Driven RUL Prognostics." PHM Society European Conference. Vol. 9. No. 1. 2026. **Education/line/direction** Masters programmes in Machine Learning, Data Science, Computer Science, Complex Adaptive Systems or similar. Number of students: 1 Start date for the Thesis project: Spring 2027 Estimated timescale: 20 weeks Contact person and supervisor Abhishek Srinivasan, Data Scientist, 08-553 816 96, [abhishek.srinivasan@scania.com](mailto:abhishek.srinivasan@scania.com) Juan Carlos Andresen,Group Manager, 08-553 835 16, [juan-carlos.andresen@scania.com](mailto:juan-carlos.andresen@scania.com) **Application** Your application should contain the following: - CV, - personal letter, - and copies of grades. Until 2026-10-31. Applicants will be assessed on a continuous basis until the position is filled. **A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.** Requisition ID: 33385 Number of Openings: 1.0 Part-time / Full-time: Full-time Permanent / Temporary: Temporary Country / Region: SE Location(s): Södertälje, SE, 151 38 Required Travel: 0% Workplace: On-site [Apply now »](https://jobs.scania.com/talentcommunity/apply/1442716033/?locale=en_US)