+++ At DLR, we are always looking for bright minds – even during the current exceptional circumstances. Our current vacancies can be found here. +++

My knowledge is shaping the future.

PhD position

Machine Learning Based Fault Detection and Isolation Framework for Liquid Propellant Rocket Engines

Starting date

1 January 2022

Duration of contract

initially 3 years


up to German TVöD 13

Type of employment


Since rocket engines operate at the limits of what is technically feasible, they are inherently susceptible to a wide variety of different faults. The immense costs associated with the loss of the launch vehicle or a test bench clearly show the importance of a suitable FDI system. Such system has to provide a proper diagnosis in real-time from existing sensor data to detect and classify abnormal behavior and, for example, to trigger an emergency shutdown.

While efforts are underway to develop model-based FDI strategies, there are cases where the exact theoretical modeling is not possible or would be very costly. A potentially promising way forward that is addressed in this thesis is to apply machine learning and especially deep learning algorithms to assist and, in some places, even replace model-based techniques. The application is challenging because the number of training data is very limited and one is practically always in the "small data regime". In addition, the measured values are noisy to varying degrees and belong to different modalities.

During your PhD you will work on the following research and development tasks, among others:

  • development and implementation of an FDI framework for liquid propellant rocket engines by the integration of machine learning with modern signal processing methods
  • study of different FDI tasks including deviating transient processes, and sensor faults
  • performing of system simulations using EcosimPro to evaluate the framework and generate synthetic training data

The infrastructure at the DLR Institute of Space Propulsion will also provide the opportunity to validate and further develop a promising FDI system through real hot fire tests of demonstrator engines.

Your qualifications:

  • Master/diploma or equivalent degree in physics, computer science, data science or similar field
  • very good programming skills (preferably python) and experience in machine learning and data analysis
  • experience with system simulation software like Simulink, Modelica or EcosimPro (preferable)
  • interest in aerospace engineering and especially space propulsion desired
  • excellent analytical skills, and the ability to work both independently and as part of a team appreciated
  • enthusiasm, motivation and creativity welcome
  • preferrably fluency in English (written and spoken)
  • basic understanding of modern liquid propellant rocket engines is an advantage

Your benefits:

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

  • Apply online now
  • You can send this job advertisement via e-mail and complete your application on a personal computer or laptop.

    We need your digital application documents (PDF). The document upload function is not supported by all mobile devices. Please complete your application on a PC/laptop.

    Complete application on PC

Technical contact

Dr. Günther Waxenegger-Wilfing
Institute of Space Propulsion

Phone: +49 6298 28-448

Send message

Vacancy 61673

HR department Lampoldshausen

Send message

DLR site Lampoldshausen

To location

DLR Institute of Space Propulsion

To institute