Intern* with Master Thesis Sensor Fusion Technology for Monitoring of Wind Turbine Drive Trains
Who we are ...
Our primary focuses at Fraunhofer IWES are on wind energy and hydrogen technologies. Our institute is home to more than 300 scientists and employees as well as over 100 students from over 30 countries pursuing careers in applied research and development at nine sites. We secure investments in technological developments through validation, shorten innovation cycles, accelerate certification procedures, and increase planning accuracy by means of innovative measurement methods.
This teams needs your support ...
You will be part of the department »System Validation for Mechanical Drive Trains« at our site in Bremerhaven. At present, our team consists of nine research associates and several students. We conduct experimental and theoretical investigations of the wind turbine nacelle at both the component level and the system level. The validation of the nacelle components can be carried out on a test rig or through simulations. Become an active member of the team; we are keen to hear your ideas! As an international oriented IWES-team, we highly appreciate an open exchange, whether this be in German or English. Respectful cooperation is also very important to us. You are wondering what you can bring to the team?
What you will do
These duties await you ...
In your master thesis, you develop a methodology to merge the measurement data from multiple sensors or multiple types of sensors. The goal is to improve fault detection in wind turbine drive trains by combining sensor signals and features to reduce the amount of uncertainty at the condition monitoring system for the wind turbine drive train. Your task will include implementation of the most suitable sensor fusion method after literature review. You will also incorporate machine learning algorithms in the sensor fusion for a high prediction accuracy. Furthermore, you will carry out experimental investigations to obtain measurement data at the test rig and investigate feature extraction methods related to the specific fault scenario triggered.
What you bring to the table
What is your background?
You are studying Mechanical Engineering, Machine Learning or a similar subject and are currently enrolled in a master’s program? You have initial knowledge of signal processing and machine learning algorithms? You can program in Phyton and MATLAB? Great! If you can speak fluent English or German, then you will fit in perfectly with us!
What you can expect
What we can offer you ...
We offer various opportunities to join us as a student. Whether it is an internship, where you gain a comprehensive insight into the areas of work, or the role of a student assistant, which is easy to combine with your studies. Are you looking for an exciting topic for your thesis and do you want to delve deeply into a topic scientifically? Together, we will find the right path for you! We know that studying can also be very demanding and requires a certain level of flexibility. That is no problem here, as – in agreement with your colleagues – you can decide flexibly what days and hours to work. Depending on the job, temporarily you can even work remotely as a student assistant.
Eager to learn more?
If you would like to find out more information about the IWES, our research aspects, and your future colleagues, please visit our career website: https://s.fhg.de/5ei
We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability.
The position is initially limited to 6 months, with the option of extension to complete a master thesis. During an internship, the working time consists of 39 hours per week. In case of a student assistantship, the working time consists of 60 hours per month. In the case of an internship, remuneration is based on the federal government guidelines for intern salaries. In the case of a student assistantship, remuneration is based on the general works agreement for employing assistant staff.
With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.
Interested? Apply online now. We look forward to getting to know you!
If you have any further questions, please contact:
People & Development
E-mail: personal@iwes.fraunhofer.de
Phone: +49 471 14 290-230
Only online applications via the portal can be considered.
Please note that we observe the provisions of the valid General Data Protection Regulation when processing applications.
Fraunhofer Institute for Wind Energy Systems
Requisition Number: 72950 Application Deadline:
Job Segment:
Wind Energy, R&D Engineer, Mechanical Engineer, Federal Government, Energy, Engineering, Bilingual, Government