Job Description
Rolls-Royce Deutschland brings together a team of more than 3,500 employees from more than 50 nations, pursuing their passion for excellence when it comes to designing, manufacturing and servicing state-of-the-art aerospace engines.
Our headquarters in Dahlewitz near Berlin are home to the company's Development, Assembly and Customer Service units as well as central administrative and strategic departments.
We deliver the best jet engines in the world. Through intelligent innovation and active collaboration, we're putting our customers at the forefront of the aviation industry and we keep them there. Our product portfolio powers more than 35 types of commercial aircraft, and with over 13,000 engines in-service around the world; we're keeping the world flying.We also offer the possibility to write your thesis and student projects together with Rolls-Royce.
Task description:
Subject: Engine Event & Signature Recognition
The goal of this master thesis is to develop, implement, and evaluate machine learning techniques for automated detection of operational events and maneuvers in multivariate time-series aero-engine data. The scope includes a review of current methods in event and maneuver detection and relevant machine learning approaches for time-series analysis. Exploration of pre-processing steps such as noise reduction, normalization, and segmentation, and identify features across multiple data channels relevant to event and maneuver recognition will be used to establish a database that can be used for training of machine learning models. The methodology, including data preparation, feature engineering, model training, and evaluation, will be tested and validated against representative aero-engine data
The deliverable of the thesis shall be a validated prototype workflow, documentation and presentation of results, and recommendations for future integration into Rolls-Royce’s data & IT setup.
Key accountabilities:
Review relevant methods for signal similarity, time-series classification, and event detection in engineering data.
Prepare and structure engine time-series data for model development, including data cleaning, feature extraction, labelling, and train/test splitting.
Develop a baseline approach for signal recognition using statistical comparison methods such as distribution tests, correlation analysis, and descriptive signal features.
Implement selected machine learning approach(es) for signal recognition or event detection, based on available labelled data.
Evaluate model performance against labelled reference data using suitable metrics.
Document and present the methodology, limitations, and recommendations for future deployment or extension of the workflow.
Qualifications:
Enrolled in a Master's programme in Data Science, Machine Learning, Computer Science, Aerospace Engineering, Mechanical Engineering, or a related quantitative field.
Good programming skills in Python, preferably with experience using pandas, NumPy, scikit-learn, and common visualization libraries.
Basic knowledge of machine learning, statistics, and time-series analysis. Interest in aero-engine performance data, signal processing, or condition monitoring.
Ability to work independently with complex datasets and document results clearly.
Good written and spoken English; German is beneficial but not required.
Pioneer the performance of the future. Join us and you’ll develop your skills and expertise to the very highest levels, working in an international environment for a company known the world over for brilliance and innovation.
Our People are our Power
We are an equal opportunities employer. We’re committed to developing a diverse workforce and an inclusive working environment. We believe that people from different backgrounds and cultures give us different perspectives. And the more perspectives we have, the more successful we’ll be. By building a culture of respect and appreciation, we give everyone who works here the opportunity to realise their full potential.
You can learn more about our global Diversity and Inclusion strategy here
So that you can be your best at work and home, we’ll consider flexible working arrangements for everyone, in any role.
http://careers.rollsroyce.de
Close date: 07.07.2026
Requisition number: JR6156104
Contact: GERRecruitmentEmergingTalent@Rolls-Royce.com
Programme
Master Thesis (Final Thesis)Job Posting Date
23 Jun 2026; 00:06
