The DAI-Labor offers possible theses for Bachelor of Science (BSc) / Master of Science (MSc) for several of its research foci. For further information and advice on the individual topics, please contact the respective supervisor.

Suggestions for theses and general questions about the process can be sent to ....

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Geodesically convex optimization has recently attracted attention from the machine learning community due to the realization that some important problems that appear to be non-convex at first glance, are geodesically convex, if we introduce a suitable differential structure and a metric. In this thesis, the student is expected to conduct an empirical study on the performance of geodesic convex optimization methods for applications in machine learning, compared to the state-of-the-art non-convex optimization techniques.


  • English language
  • successfully completed courses Machine Learning I and II
  • Programming skills in Python
  • Knowledge in Differential Geometry and Convex Optimisation
supervisor / Contact person
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The Electrical Grid plays an important role in our everyday life. As to bring the new technologies in place and facilitate the energy transition, Simulations have been conducted to assess the feasibility of new developments. Today we do not only talk about the stability, reliability and efficiency of the grid but also its resilience or the ability to Anticipate, React, Adapt, and Recover. Therefore, to increase the resilience of today’s power grid, simulation and software assessments are needed to couple the multidomain inter-dependencies of the Cyber-Physical Human Systems (CPHS). Extreme weather events have been occurring more frequently around the world. These events demonstrated how vulnerable the distribution grid is.

Well-placed and coordinated enhancements, such as system hardening and redundancy, orchestrated microgrids, and advanced fault detection, can reduce the number of outages that could occur due to High Impact Low Probability (HILP) events. We offer a Bachelor / Master Thesis in the field of Power Grid Simulation and integration of Distributed Energy Resources. The thesis will encapsulated the different stages from Model Development to Vulnerability Assessment, and Power Flow Optimization related to Distributed Energy Resources in Power Networks.


  • Studies in the fields of technical informatics, control engineering, automation, electrical engineering, energy engineering, or similar
  • Good knowledge and practical experience in python, co-simulation paradigms, ArcGIS, Simulink or Power Factory
  • Teamwork and Goal Oriented
  • Interest in Renewable Energy Topics and Automation Technology
  • Task Oriented and Independent Learning

research area

Energy Data Analytics
supervisor / Contact person
M.Sc. Izgh Hadachi
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A user interface for the semantic recommendation system "Universal Recommender" is to be programmed.

The Universal Recommender is a Java library developed at the DAI-Labor, which can be used to program recommendation systems for semantic data sets. A graphical user interface is to be programmed with which the following tasks can be performed:

  • Visualization of data sets (e.g. degree distributions, 2D embeddings, etc.)
  • Selection of algorithms and parameters
  • Automatic evaluation of recommendation systems
  • Persistence of recommendation configurations that can be embedded in a running recommendation system.

On the servers of the DAI-Laboratory there are several data sets that can be used. The Universal Recommender is available as a Java module. The graphical interface can be programmed as a desktop UI or as a web application.

Required knowledge

  • Java
  • A UI library or web programming (e.g. Swing, SWT, Richfaces, etc.)
  • Basic knowledge in the area of data mining/visualization of data

research areas

Semantic Web Technologies in Multi-Agent Systems Recommender Systems
supervisor / Contact person
Dr.-Ing. Andreas Lommatzsch