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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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. Together with our team of experienced scientists, postdocs and PhD students, you will develop materials that contribute to the development of the next generation of bio-based hybrid materials. The goal
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. Together with our team of experienced scientists, postdocs and PhD students, you will develop materials that contribute to the development of the next generation of advanced hydrogels for wound care
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with our team of experienced scientists, postdocs and PhD students, you will develop materials that contribute to the development of the next generation of sustainable biocomposite materials. This project is
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of catalytic coatings for water treatment. As a PhD candidate, you will: Develop novel and robust catalytic materials for efficient removal of emerging contaminants in water. Learn and apply advanced materials
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will: Develop novel and robust catalytic materials for efficient removal of emerging contaminants in water. Learn and apply advanced materials characterization techniques. Work with different analytics
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campaigns (one to two weeks per year) Holder of a valid car driving license Our offer We offer a fully funded PhD student position (3 years, full time) at Empa, the Swiss research institute for materials