The Fraunhofer IEE in Kassel conducts research in the fields of energy economics and energy system technology, focusing on: energy informatics, energy meteorology and geoinformation systems, energy economics and system design, energy process engineering and storage, grid planning and grid operation, grid stability and power converter technology, and thermal energy technology. Around 450 scientists, employees, and students develop solutions for the energy transition and generate around €40 million in revenue per year.
Would you like to help shape the implementation of the energy transition and work at the interface between science and industry?
Then join the KonSEnz team and work on the development of innovative, self-learning forecasting systems for smart energy markets. You will be involved in the integration of microservices into a scalable architecture that enables continuous learning and adaptation to real-world changes. You will contribute to the forecasting of generation and power flows in order to develop solutions that promote efficiency and flexibility in grid operation and energy marketing. In doing so, you will have the opportunity to play a direct role in implementing the energy transition and shaping innovative approaches for the future of energy supply.
What you will do
Development and evaluation of artificial intelligence models as part of a master's thesis on the topic of “Continuously self-learning prediction models” with the following focus areas:
* Design and development of methods for drifter detection in self-learning AI models
* Evaluation using real data sets from photovoltaic systems and transformers
* Analysis and interpretation of model results to improve forecast accuracy
* Implementation in existing software architectures
What you bring to the table
* Currently enrolled in a master's degree program in computer science, electrical engineering, mathematics, or a comparable field of study
* Good knowledge of the Python programming language
* Solid knowledge in at least one of the following areas: machine learning, data analysis, energy technology
* Experience with common deep learning and data analysis frameworks (e.g., PyTorch, Numpy, Pandas, sklearn, etc.)
* Independent, structured, and reliable way of working
* Very good German or English skills
What you can expect
* Insight into application-oriented research in the energy sector with direct relevance for industry and society
* Access to expert networks in the energy industry and research
* Commitment to the Diversity Charter – we actively promote diversity and inclusion in all areas
* Option of continued employment (e.g., as a research assistant or for a doctorate)
* Flexible and individual adjustment of working hours to lecture and exam times
as well as the option to work from home or in the modern institute building with a New Work concept in a central location
As part of your master's thesis, you also have the opportunity to work as a research assistant for a number of hours that suits you (40 to 80 hours). The position is initially limited to 12 months, although we aim to establish a longer-term collaboration. Exceptions for a shorter term are fundamental operational reasons, the personal wishes of the student, or the end of their studies. 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 (including resume, cover letter, certificate of enrollment, and transcript). We look forward to getting to know you!
Further questions will be answered by:
Luzie Barten (Recruiting)
Tel.: +49 (0) 561 / 7294 - 1672
Fraunhofer Institute for Energy Economics and Energy System Technology IEE
Requisition Number: 80106 Application Deadline: 07/09/2025