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The Max Planck ETH Center for Learning Systems (CLS) addresses cross-disciplinary research questions in the design and analysis of natural and man-made learning systems. The excellent engineering
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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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with competitive salary according to ETH standards Interdisciplinary and international research environment You can expect numerous benefits , such as public transport season tickets and car sharing, a
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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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kinetics, coarse-graining across time and length scales, machine learning from small and multi-fidelity datasets, and autonomous discovery workflows. You will collaborate with experimentalists at ETH Zurich
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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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23 Jan 2026 Job Information Organisation/Company ETH Zürich Research Field Computer science » Computer architecture Computer science » Programming Computer science » Other Engineering » Biomedical
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15 Jan 2026 Job Information Organisation/Company ETH Zürich Research Field Biological sciences » Other Chemistry » Other Environmental science » Earth science Physics » Chemical physics Physics
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of applying molecular models at process scales, the project combines efficient mathematical concepts like automatic differentiation with backpropagation – the same concept that powers machine learning and