Student* with Master Thesis (optional) Model Development for Early Fault Detection on Wind Turbines

Student* with Master Thesis (optional) Model Development for Early Fault Detection on Wind Turbines

Hannover

Wind Energy Systems

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 800 employees work with an annual research budget of 3.0 billion euros.

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 team needs your support …
You will be part of the group »Technical Reliability« at our site in Hannover. At present, our team consists of eight research associates and several students. The aim of our group is to make wind turbines more reliable. We mainly investigate causes of failures and approaches to detect or prevent faults at an early stage. We analyze failure and operating data and carry out extensive field measurements. 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. 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 …
You will be responsible for developing data-based models for the early detection of faults in wind turbines. In particular, you will implement normal behavior models that utilize the physical relationships in operating data. Your role will involve using physical modeling as a foundation, supported by physics-informed machine learning for model optimization. Additionally, you will classify data based on service and maintenance data. You will perform all analyses with Python, incorporating modules such as Pandas, Numpy, Scikit-Learn, Tensorflow, and PyTorch. Your work will be based on high-resolution operating data from wind turbines stored in a PostgreSQL database.

What you bring to the table

What is your background?
You are studying Mechanical Engineering, Energy Technology, Technical/Applied Computer Science, or a similar subject and are currently enrolled in a master’s program? You have a basic knowledge of wind turbines and of wind turbine design and operation? You know your way around fundamentals of physics and material behavior? Great! Besides, you can program in Python? Perhaps you have a basic understanding of machine learning and AI fundamentals. Have you even come into contact with machine learning toolkits such as Tensorflow or PyTorch? Great! If you are ready to tackle challenges and contribute to cutting-edge projects, we invite you to join 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. The working time consists of 60 hours per month. Remuneration according to 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: 73777 Application Deadline: