Machine learning engineer Jobs in Kolkwitz

The Fraunhofer Research Institution for Energy Infrastructures and Geothermal Energy IEG conducts research at seven locations in the fields of integrated energy infrastructures, geothermal energy and sector coupling for a successful energy transition. Our research institute conducts applied research, develops innovative technologies for public and industrial clients and translates these into marketable products and processe

In light of the conclusions presented in the Heat Roadmap Europe, it is evident that the collective heat demand across 27 European countries constitutes approximately 50% of the final energy consumption. Consequently, the heat sector takes a pivotal role influencing European CO2 emissions, underscoring the imperative for a renewable energy driven heat supply to effectively address the challenges posed by global warming. In this context, district heating networks (DHNs) play a major role since they can realize fully decarbonized heat supply utilizing various distributed heat sources. The principle of operation of a DHN is the transport of water heated by the decentralized producers to the consumers, who can extract heat from this water and thereby lower the water temperature. The state-of-the-art (SoA) operation of a producer is primarily characterized by the temperature that the producer injects into the DHN, i.e., the producer's supply temperature. The set point for the supply temperature is often derived from a heating curve that defines a static correlation between measurable parameters, e.g. the ambient temperature, and the set point for the supply temperature.

Compared to the SoA operation utilizing static heating curves, optimization-based predictive operating strategies (OBPOSs) promise better performance. However, a very limited degree of digitalization in many DHNs makes it hard or even impossible to handle the signals needed for implementing an OBPOS. Nevertheless, mathematical models of DHNs allow to simulate the behavior of DHNs under predictive operation.

The main objective of this master thesis is to investigate whether the resulting simulation data can be used for the training of ML-based controllers that approximate OBPOS and require less implementation effort.

The master thesis will be supervised by Fraunhofer IEG scientists at the Bochum or Cottbus sites. If you are interested in this master thesis in an open and diverse team and want to build the bridge from applied to basic research, apply now!

What you will do

• Literature review on mathematical modeling and operation of district heating networks.
• Literature review on machine and deep learning methods for the prediction of heating curves or similar objectives.
• Implementation of a district heating network model including static heating curves as well as optimization-based operating strategies. The generated data will be used for training of a neural network.
• Setup of a machine learning-based heating curve using different suitable algorithms to approximate optimal producer’s supply temperature set points.
• Simulation case study to evaluate performance.
• Documentation of the results.

What you bring to the table

What you can expect

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!

If you have any questions about this position, please contact :

Henning Knauer and Max Rose

If you have any questions about the application process, please contact :

Philipp Steinborn

Phone : +49 355 35540 172

Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG

Requisition Number : 78954                Application Deadline : 04 / 30 / 2025