Project background
This thesis aims to answer the following research questions:
R1: Which ML concept is the most promising in developing a GridFM? In particular, the thesis aims to investigate:
R1.1 Can physics-informed learning play a role in building GridFM?
R1.2 Can we exploit neural operators in building GridFM?
R1.3 Can we leverage Reinforcement Learning (RL) in building GridFM?
For the latter, the thesis will investigate whether current LLM architectures, which rely on RL, can also be used to develop a robust GridFM architecture.
R2: How can the mixture of experts (MoE) paradigm be utilized in building GridFM? The current success of DeepSeek has demonstrated that the MoE Transformer architecture can significantly improve inference efficiency and model scalability. Developing and testing such an architecture will be crucial to GridFM's development.
R3: Which downstream tasks will benefit the most? The thesis will identify a set of related power system tasks for which a single MoE will be developed.
Additional research questions:
R4: What datasets and data structures will be of most benefit to GridFM? The theses will contribute to the gathering and generation of real and synthetic datasets, respectively. This data will aim to better reflect real-world conditions while ensuring applicability across various operational planning and optimization tasks.
This work will be performed using currently developed or in-development tools at RRE. Namely, the PowerGraph dataset will be initially used and further extended. The current modeling tool for grid analyses, Cascades and its surrogates, developed using graph neural networks (GNN), will serve as a starting point for future modeling developments. In addition, the candidate will leverage our experience and models developed using RL and physics-informed neural networks. This work will further benefit from our ongoing collaborations with IBM and other partners.
Job description
* The PhD project aims to develop next-generation AI foundation models for the electric power grid to support a secure and sustainable energy transition
* The candidate will build and adapt large-scale machine learning models that generalize across multiple power system tasks, including operations and planning, as well as risk assessment
* The work includes developing new data resources, designing scalable AI architectures, and evaluating their real-world applicability in collaboration with industry stakeholders
* The project sits at the intersection of power engineering and artificial intelligence, contributing to reliable, resilient, and low-carbon energy systems
Profile
* We are looking for a candidate with an MSc degree (or close to completion) in Power Engineering, Computer Science, or a related field
* The candidate has a solid quantitative background in either power system analysis or machine learning (knowledge of both is ideal but not required)
* Experience with programming in Python (or similar languages) and familiarity with data-driven modelling approaches are beneficial
* The candidate should be strongly motivated to work at the interface of power systems and advanced AI, demonstrate strong analytical and problem-solving skills, and be able to work independently within an interdisciplinary research environment
* Excellent written and spoken English is required
We offer
* Fully funded position with competitive salary according to ETH standards
* Access to state-of-the-art computational infrastructure
* Interdisciplinary and international research environment
* Perspectives for career development
* International teamculture and team composition
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