The Fraunhofer IEE in Kassel conducts research in the fields of energy economics and energy systems technology with a focus on: Energy informatics, energy meteorology and geo-information systems, energy economics and system design, energy process engineering and storage, grid planning and operation, grid stability and power converter technology as well as thermal energy technology. Around 450 scientists, employees and students develop solutions for the energy transition and generate around 38 million euros in revenue per year.
We are seeking a proactive and detail-oriented student to support the development and validation of digital twins for offshore wind farms as part of the EU project WinDTwin, focused on predicting electricity generation from wind energy. Your primary responsibility will be to contribute to the creation and deployment of advanced AI and statistical models for performance prediction through digital twins, utilizing innovative artificial intelligence methods for precise, dynamic, and adaptive forecasting.
What you will do
* Assist in the development and validation of advanced AI and statistical models for energy forecasts in offshore wind farm digital twins.
* Utilize AI and statistical techniques to extract insights from large time series datasets.
* Collaborate within an interdisciplinary team to develop innovative algorithms for forecasting, ranging from short-term to seasonal predictions.
* Create prediction modules for digital twins and support their deployment using containerization technologies.
What you bring to the table
Currently pursuing a master’s degree in data science, Applied Mathematics, Computer Science, Physics, Meteorology, or a related field.
Strong interest in wind energy development and the energy transition.
Proficient in Python or similar object-oriented programming languages.
Experience with machine learning/statistical methods applied to time series data is advantageous.
Familiarity with databases, containerization, and knowledge of MLOps is a plus.
Goal-oriented with strong analytical skills, team spirit, and excellent communication in English or German.
Independent, self-motivated, and enthusiastic about interdisciplinary collaboration.
Interest in scientific research and the potential for a master’s thesis.
What you can expect
* Engage with an international consortium of 13 organizations across 7 EU member states.
* Gain hands-on experience in the renewable energy sector with practical applications.
* Receive mentorship from experienced professionals in data science and energy forecasting.
* Opportunities for scientific development and support for a master’s thesis.
* Join a motivating team in a positive working environment.
* Enjoy flexible working hours and the option for remote work to maintain work-life balance.
* Work in a modern facility that fosters a productive and inspiring atmosphere.
The monthly working hours range from 40 to 80 hours. The position is initially limited to 12 months, with the possibility of a longer-term collaboration. 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. 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!
Questions about this position will be answered by:
* Abhinav Tyagi (Tel.: +49 (0) 561 7294-1664)
Fraunhofer Institute for Energy Economics and Energy System Technology IEE
www.iee.fraunhofer.de
Requisition Number: 78456 Application Deadline: 03/31/2025