Master Thesis - Smart Energy Solutions: Optimize Power Systems Machine Learning Techniques

At the Institute of Climate and Energy Systems - Energy Systems Engineering (ICE-1) we focus on the development of models and algorithms for simulation and optimization of decentralized, integrated energy systems. Such systems are characterized by high shares of renewable energies and increasing sector coupling, which leads to high spatial and temporal variability of energy supply and demand as well as a high degree of interdependence of material and energy flows.

Our research aims to provide scalable and faster-than-real-time capable methods and tools that enable the energy-optimal, cost-efficient and safe design and operation of future energy systems.

The growing demand for energy transformation to counter the effects of climate change and reduce dependence on imported energy sources and raw materials is driving the European energy system towards a dynamic, supply- and demand-driven approach, often organized in local energy communities with a high penetration of renewable resources.

Power grids are critical infrastructure requiring ultra-reliable state estimation and control. With rising renewable energy integration, operators need certifiable performance guarantees to prevent blackouts while optimizing grid efficiency. Current models struggle to quantify uncertainty, risking overconfidence in volatile scenarios.

The goal is to scale and enhance Bayesian Distribution System State Estimation using both simulated and, if necessary, real-world grid data.

• Develop a distributed version of an existing Bayesian Distribution System State Estimation algorithm
• Validate and compare your method on critical scenarios via a Real-Time simulator and or real data

• Excellent university degree (Bachelor) and ongoing Master studies in the field of Data Science or a comparable field i.e. Electrical Engineering, Computer Science/ Engineering, Physics
• Strong mathematical background
• Interest in energy systems, power grids and its components
• Excellent knowledge and experience in programming Python
• Excellent knowledge and experience in machine learning
• Experience with git is welcome
• Excellent ability for cooperative collaboration
• Very good communication skills in English
• Prior German knowledge is not strictly required

• SCIENTIFIC ENVIRONMENT: A highly motivated research group in one of the biggest research centers in Europe
• PRACTICAL RELEVANCE: With us, you will gain valuable practical experience alongside your studies and actively participate in interdisciplinary projects
• OUTSTANDING INFRASTRUCTURE: An excellent scientific and technical infrastructure: both necessary conditions for a successful Master thesis
• ACTIVE INVOLVEMENT: Participation in project meetings and, if necessary, conferences
• SUPPORT: Strong support and mentoring for setting up a future career in science and/or the industry
• FLEXIBILITY: The opportunity to work flexibly (in terms of location), e.g. partly from home
• SUPPORT FOR INTERNATIONAL EMPLOYEES: Targeted services for international employees, e.g. through our International Advisory Service
• CONTRACT DURATION: The position is for a flexible term of 0,5 - 1 year.
• FAIR REMUNERATION: We will pay you a reasonable remuneration for your thesis

In addition to exciting tasks and a collegial working environment, we offer you much more:

A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.

The following links provide further information on diversity and equal opportunities:  and on specific support options: