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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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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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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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upload all documents through our webpage, applications via E-mail cannot be accepted. Where to apply Website https://academicpositions.com/ad/empa/2026/phd-position-on-all-solid-state-batt… Requirements
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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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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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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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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
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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