Join a leading research team focused on creating sustainable, resilient, and equitable urban energy systems. You will work at the intersection of technology, policy, and real-world urban challenges, contributing to innovative solutions with real impact. This position is offered in collaboration with top research groups at Empa and EPFL.
Your tasks
- Design and develop physics-informed hierarchical graph neural network architectures for complex, multi-scale urban energy infrastructures.
- Explore how these models can represent spatial and temporal dependencies in systems like building energy demand, district heating and cooling, storage, and local electricity grids.
- Translate deep learning innovations into practical tools for sustainable urban energy systems.
- Support applications in forecasting, system optimization, flexibility management, and resilience analysis.
- Work closely with interdisciplinary teams at Empa and EPFL, as well as external academic and industry partners.
What you bring
- Master’s degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related field.
- Strong analytical background with skills in geometric deep learning, signal processing, statistics, or learning theory.
- Knowledge of energy systems, multi-energy infrastructures, or urban energy applications is a strong asset.
- Self-driven, creative, and strong problem-solving skills.
- Ability to work in an interdisciplinary environment.
- Proficiency in English (spoken and written).
- Good comprehension and oral skills in German are desirable.
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Über uns
Empa is a leading research institution of the ETH Domain with around 1,100 employees, dedicated to advancing materials science and technology for a sustainable future. The Urban Energy Systems Laboratory pioneers innovative strategies and solutions for real-world urban challenges, combining technology, policy, and practical implementation.