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will require a high degree of innovativeness in binder research, including mastery of asphalt binder chemical and mechanical characterization, production process optimization while consid-ering
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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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a PhD thesis at ETHZ Your profile We are looking for a highly motivated, committed, and talented candidate. The successful candidate should hold an MSc (or equivalent) in chemistry, physics
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who thrives in a collaborative and interdisciplinary research environment. The ideal candidate possesses a PhD degree in chemistry or chemical engineering, materials science, physics, or related
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energy-related applications. Our research portfolio spans fundamental materials chemistry, process–structure–property relationships, and application-driven R&D, in close collaboration with academic and
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system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical, spatial, and infrastructural constraints. The work aims to identify
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energy-related applications. Our research portfolio spans fundamental materials chemistry, process–structure–property relationships, and application-driven R&D, in close collaboration with academic and
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of students Your profile University Master's degree in process engineering, chemical engineering, mechanical engineering or materials science with some (CFD) experience. Good technical knowledge and/or high
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that will shape your research career. Your profile We are looking for 2 highly motivated PhD students with a strong analytical background and an MSc degree in Physics, Computational Chemistry, Materials
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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