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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
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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
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on the development of new sensor concepts, their experimental characterisation, the development of suitable methodologies and systematic validation under realistic operating conditions. This includes the setup
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development of electrochemical sensors detecting environmental pollutants, providing real-time information for effective management. Past and current work includes electrochemical sensors for airborne virus
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teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development
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heritage. Muoniverse brings together 30 research teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates
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communication, are required for the project. Our offer Empa offers outstanding infrastructure, career support, networking opportunities, and competitive salaries. Empa also provides excellent infrastructure and
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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
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infrastructure, career support, networking opportunities, and competitive salaries. The position is available from April 2026 or after negotiation with a duration of four years. We live a culture of inclusion and
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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