This PhD position is offered in collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL, and Urban Energy Systems Laboratory (UESL) at EMPA.
Application link: https://apply.refline.ch/673276/2121/pub/1/index.html
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 profile
You 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.