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scale description of interfaces with an application to nucleation phenomena. The MET group at ETH Zurich, led by Philipp Rehner, is dedicated to linking rigorous physical molecular models to the design of
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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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: Which ML concept is the most promising in developing a GridFM? In particular, the thesis aims to investigate: R1.1 Can physics-informed learning play a role in building GridFM? R1.2 Can we exploit neural
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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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100%, Zurich, fixed-term The Atmospheric Physics group at the Institute for Atmospheric and Climate Science (IAC), ETH Zurich invites applications for a PhD position (3-4 years) investigating
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–2028 cycle will open in November 2026. Help Us Improve Our Fellowship Application Process We noticed that some applicants began the application process but did not submit their application
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deposition process parameters govern film architecture (e.g., thickness, porosity, roughness, grain/particle connectivity, interfaces, adhesion, and pattern fidelity) and how this architecture ultimately
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range of topics related to imaging with geophysical data. Our research focuses on mathematical methods for processing, imaging, and inversion of geophysical data, the physics of wave propagation, and the
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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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off-site and on-site robotic additive manufacturing process for sustainable, low-carbon construction.To support the commercialization and technical development of this technology, we are looking for a