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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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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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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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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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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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subtype of dementia. Your tasks This interdisciplinary PhD project combines atomic force microscopy, fluorescence microscopy, chemical spectroscopy, biophysics and data analytics. The PhD student will be
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(R1) Application Deadline 22 Jun 2026 - 21:59 (UTC) Country Switzerland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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. Empa is a research institution of the ETH Domain. Empa’s Laboratory Materials for Energy Conversion focuses on materials and device innovation for sustainable energy technologies. Your tasks This PhD
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their lifetime in the environment. This PhD project aims to examine the atmospheric aging and in-cloud scavenging processes of micro- and nano-sized plastic particles through laboratory studies. Your