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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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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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perform quantitative hydrogen analysis using electron spectroscopy. Main task of the PhD student is to establish the scientific basis for high-resolution electron recoil spectroscopy for surface hydrogen
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. Empa is a research institution of the ETH Domain. Applications are invited for a PhD position in the Air Quality and Particle Technology group (Prof. Dr. J. Wang). The successful candidate will work in
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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. Empa is a research institution of the ETH Domain. The Biointerfaces Laboratory is offering a PhD position focused on development of engineered antimicrobial hydrogels. This project aims to tackle
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activation and micromechanical modeling Progressive damage modeling of reinforced FRPs Mechanical characterization and fracture experiments Complete a PhD thesis at ETHZ Your profile Highly motivated and
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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
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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