Master's Thesis

Foundation models (FMs) have revolutionized fields such as natural language processing and computer vision. Yet, their application in scientific and engineering domains — particularly in power systems — remains at an early stage. This master’s thesis offers a unique opportunity to pioneer Grid Foundation Models (GridFMs) to transform the operation of electric grids.

The use of self-supervised learning and graph-transformer neural networks have the potential to speed-up the modeling of the electrical grid, load and generation forecasting, dispatch optimization, and real-time control room decisions. These innovations are essential to manage the increasing complexity of power systems, driven by the integration of renewable energy, distributed generation, the rise of electric mobility, and the need to support energy-intensive infrastructure such as data centers powering generative AI.

We are looking for a motivated master's student with a passion for developing foundation models for the electric grid. The ideal candidate brings a positive, solution-oriented mindset and is eager to tackle complex, real-world challenges. You will join the AI for Climate Impact group at IBM Research in Zurich , working at the intersection of cutting-edge AI and energy systems. As part of IBM’s global AI science community, you will have access to world-class expertise and high-performance computing infrastructure. Your code will likely be open-sourced and contribute to a high-impact Linux Foundation for Energy project.

Your Profile

• Currently enrolled in a Master’s program in Computer Science, Electrical Engineering, Mathematics, or Physics.
• Strong programming skills in Python and experience with PyTorch.
• Familiarity with GitHub/GitLab for code collaboration and version control.
• Creativity and motivation to explore physics-informed machine learning architectures.
  
  

Bonus Skills

• Knowledge of power system fundamentals and simulation tools (e.g., PandaPower).
• Experience with HPC job scheduling environments.
• Prior work with large-scale models (e.g., Transformers, GNNs).
  
  

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

Please submit your application through the link below. We encourage candidates to also share a 3-minute video, in which they introduce themselves, as well as highlight their motivation and expertise. The video is not mandatory.

If you have any question related to this position, please contact Dr. Thomas Brunschwiler , E-Mail schreiben.

Key Reference
Foundation Models for the Electric Power Grid , Cell Joule 8, 12 (2024)