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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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essential, while experience with machine learning is advantageous but not strictly required. Excellent English skills, both in verbal and written communication, are required for the project. We are looking
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training, exchanges, and career development for PhD students and postdocs. Learn more at https://www.muoniverse.ch/ . Muoniverse positions often serve as bridges between individual research groups and
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for PhD students and postdocs. Learn more at https://www.muoniverse.ch/ . Muoniverse positions often serve as bridges between individual research groups and institutions, supported through dedicated
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written and spoken. Experience with experimental fluid mechanics and computer vision is an advantage. Our offer We offer a stimulating, multidisciplinary research environment within the ETH Domain, with
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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
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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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14 Feb 2026 Job Information Organisation/Company Empa Research Field Computer science » Other Engineering » Other Mathematics » Applied mathematics Technology » Energy technology Technology
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First Stage Researcher (R1) Country Switzerland Application Deadline 23 Mar 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme