> Markdown version of [/jobs/ext/2669768-master-thesis-benchmarking-and-transferability-of-grid-foundation-models-for-power-grid-analysis](https://www.wearedevelopers.com/jobs/ext/2669768-master-thesis-benchmarking-and-transferability-of-grid-foundation-models-for-power-grid-analysis). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Master Thesis - Benchmarking and Transferability of Grid Foundation Models for Power Grid Analysis - **Company:** Forschungszentrum Jülich GmbH - **Location:** Jülich, Germany - **Contract:** Internship / Graduate position - **Skills:** Artificial Neural Networks, Computer Programming, Data Validation, Python (Programming Language), Machine Learning, Tensorflow, Data Processing, Pytorch, Transfer Learning, Large Language Models, Information Technology, Data Analytics - **Published:** September 1, 2026 - **Apply:** https://www.myability.jobs/de/node/191497/apply-external ## About the Role Currently enrolled in a Master's program in electrical engineering, energy systems, computer science, data science, applied mathematics, industrial engineering, or a related field, and have demonstrated very good academic performance in your studies. You are also characterized by: * Strong interest in power grids, machine learning, and the energy transition * Good understanding of power systems, power flow, distribution grids, or energy system modeling * Basic understanding of graph neural networks, foundation models, data-driven modeling, or physics-informed machine learning * Good programming skills in Python * Experience with machine learning frameworks such as PyTorch, PyTorch Geometric, or similar tools is an advantage * Experience with power-system tools such as pandapower, PyPSA, PowerModels, or similar frameworks is an advantage * Independent, structured, and reliable way of working * Good analytical skills and data processing skills * Very good command of English ## Description Power grids are naturally represented as graphs, where buses, lines, transformers, generators, and loads interact through physical constraints. Recent developments in graph neural networks, and physics-informed machine learning open new possibilities for establishing grid foundation models. However, it is still unclear which model structures is most capable and suitable, how well they generalize across different grid topologies, and whether models trained on benchmark grids can be transferred to real distribution networks. In this thesis, you will investigate these questions by designing and evaluating machine learning models for benchmark grids and, depending on data readiness and project progress, by testing them on a real campus network with high-quality consumption and voltage measurements. Within this Master thesis, you will contribute to the evaluation of GridFM-inspired machine learning approaches for power grid analysis. The main goal is to develop a reproducible benchmarking workflow and to compare different model structures under realistic grid-analysis tasks. The work may include the following tasks: * Review current approaches for Grid Foundation Models, graph neural networks, and physics-informed machine learning in power systems * Set up benchmark grids and generate or process suitable simulation data for power-flow, voltage-prediction, or state-estimation tasks * Implement and compare different model structures, such as graph neural networks, graph transformers, multilayer perceptrons, or other suitable architecture * Evaluate model performance with respect to prediction accuracy, physical feasibility, robustness, runtime, data efficiency, and transferability to unseen grid topologies * Investigate whether pre-trained or benchmark-trained models can be adapted to a real campus network using available consumption and voltage measurements * Analyze limitations and derive recommendations for future GridFM development and practical application in distribution-grid analysis * Document the workflow and results in a reproducible and scientifically sound way The exact focus of the thesis can be adapted to your interests and background. Possible directions include architecture benchmarking, topology generalization, physics-informed training, transfer learning, or real-data validation on the campus network. If desired, the thesis can be preceded by an (mandatory) internship phase, allowing you to become familiar with the topic, tools, benchmark grids, and available data before starting the Master thesis. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Graph Neural Networks: What’s behind the Hype?](https://www.wearedevelopers.com/videos/474-graph-neural-networks-what-s-behind-the-hype) ## Related Articles - [Best Coding Boot Camps in Germany](https://www.wearedevelopers.com/magazine/237-best-coding-boot-camps-in-germany) - [A Guide to Green Tech and Green IT Careers](https://www.wearedevelopers.com/magazine/374-a-guide-to-green-tech-and-green-it-careers) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [System change: restart as developer?](https://www.wearedevelopers.com/magazine/39-system-change-restart-as-developer) - [Backend Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/196-backend-developer-salary-in-germany-2023) - [Top 6 Hackathons for Developers in 2023](https://www.wearedevelopers.com/magazine/263-top-6-hackathons-for-developers-in-2023)