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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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online application including a letter of motivation, CV, certificates, diplomas and contact details of two reference persons. Please submit these exclusively via our job portal. Applications by e-mail and
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-informed deep learning algorithms, one publication (e.g. MSc thesis or preferably a conference/journal publication, link is sufficient). Please submit these exclusively via our job portal. Applications by e
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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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(in English), one publication (e.g., MSc thesis or a conference/journal publication, link is sufficient). Please submit these exclusively via our job portal. Applications by e-mail and by post will not
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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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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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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system