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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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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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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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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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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
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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