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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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required. Our offer We offer this postdoc position at a research institution with excellent infrastructure and broad interdisciplinary surrounding. Position starting from June 2026 or upon agreement. We live
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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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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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(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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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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. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2025/phd-position-on-antimicrobial-materi… Requirements Research FieldChemistryYears
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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
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of Zurich and Wageningen University & Research. The four-year STEPS project focusses on developing data-driven and machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite