PhD Position in Hierarchical Graph Neural Networks for Multi-Scale Urban Energy Systems

Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain.The Urban Energy Systems Laboratory (UESL) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and equitable urban energy systems. Our work combines technology and policy with systems thinking and practical implementation, always grounded in real-world urban challenges.This PhD position is offered in collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance, reliability, and availability of complex industrial systems while making maintenance strategies more cost-efficient.Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures across different spatial and temporal scales, from building-level energy demand to district-scale interactions and their integration with wider energy networks.Your tasksThe focus of this research is to design and develop (physics-informed) hierarchical graph neural network architectures that can capture the complexity of multi-scale urban energy infrastructures. The PhD will explore how these models can represent spatial and temporal dependencies in systems, such as building energy demand, district heating and cooling, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting, system optimization, flexibility management, and resilience analysis. The work will be carried out in close collaboration with our interdisciplinary teams at both Empa and EPFL, as well as external academic and industry partners.Your profileYou are a highly motivated and talented candidate with a Master's degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy infrastructures, or urban energy applications is a strong asset. You are self-driven, creative, and bring strong problem-solving skills as well as the ability to work in an interdisciplinary environment. Proficiency in English (spoken and written) is required; good comprehension and oral skills in German are desirable.Our offerWe offer a multifaceted and challenging PhD position in a modern research environment with excellent infrastructure. The candidate will benefit from joint supervision by Prof. Olga Fink (EPFL IMOS) and the UESL team at Empa, combining cutting-edge expertise in machine learning and energy system modeling with strong ties to academic and industry partners. The PhD is intended to be formally enrolled at EPFL. The ideal starting date is January 2026, or upon mutual agreement.We look forward to receiving your complete online application including a letter of motivation, an up-to-date CV, transcripts of all obtained degrees (in English), a brief research statement (one page) describing your project idea in the field of physics-informed deep learning algorithms, one publication (e.g. MSc thesis or preferably a conference/journal publication, link is sufficient). Please submit these exclusively via our job portal. Applications by e-mail and by post will not be considered.PatriciaNitzsche,Stv. Leiterin Human Resources / Dep. Head Human Resources