PhD Position in Operational Feasibility and Robustness of AI- and Multifractal-Inspired 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 part of the FRACNET‑CITY project, a French‑Swiss research collaboration funded by the French National Research Agency (ANR) and the Swiss National Science Foundation (SNSF). The project brings together expertise in multifractal analysis, urban and energy planning, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land‑use constraints such as No Net Land Take. The consortium comprises four complementary PhD projects addressing different methodological and thematic aspects of urban energy systems.
The successful candidate will be based at Empa (Switzerland) and work in close collaboration with partner institutions in France and Switzerland, including research stays within the FRACNET‑CITY consortium.
Your tasks
The focus of this PhD position is to assess the operational feasibility and robustness of urban energy system designs emerging from AI‑ and multifractal‑informed planning approaches. The PhD will develop and apply optimization‑based energy system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical, spatial, and infrastructural constraints.
Your profile
You are a highly motivated and qualified candidate holding a Master’s degree in Engineering, Energy Systems, Applied Mathematics, Operations Research, Physics, or a related field. You bring a strong analytical background and experience with optimization models and quantitative analysis methods. Familiarity with urban energy systems, energy infrastructures, or spatial modelling and analysis is a strong asset.
You are proficient working with programming languages such as Python, MATLAB, or similar, and are interested in combining analytical modelling with data‑driven or AI‑based approaches. You are self‑driven, curious, and able to work both independently and in interdisciplinary research teams. Proficiency in English (spoken and written) is required; knowledge of German and/or French is an advantage.
Our offer
We offer a challenging and stimulating PhD position in a dynamic and interdisciplinary research environment with excellent infrastructure. The successful candidate will be embedded in the Urban Energy Systems Laboratory (UESL) at Empa and benefit from close collaboration within the FRACNET‑CITY French‑Swiss research consortium, providing access to complementary expertise in energy systems, spatial analysis, and artificial intelligence.
The PhD position will be formally hosted at Empa, with opportunities for research stays at partner institutions in Switzerland and France. The starting date is flexible, ideally in September 2026, or upon mutual agreement.
We live a culture of inclusion and respect. We welcome all people who are interested in innovative, sustainable and meaningful activities – that’s what counts.
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), one publication (e.g., MSc thesis or 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.
Job details
Title
PhD Position in Operational Feasibility and Robustness of AI- and Multifractal-Inspired Urban Energy Systems
Empa, the Swiss Federal Laboratories for Materials Science and Technology, conducts cutting‑edge materials and technology research.
#J-18808-Ljbffr