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insights beyond conventional techno-economic approaches. Your tasks The focus of this research is to advance energy system models by systematically embedding Social Sciences and Humanities (SSH) aspects
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of nanoparticles for healthcare and industrial applications. As a PhD candidate, you will: Develop and refine SAXS and FCCS methods to quantify size, concentration, density and internal structure of diverse
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, technologies and systems. The ERAM group within TSL have great experience in SSbD, especially in combining different methods such as modeling mass flows analysis (MFA), Life cycle analysis (LCA) and semi
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different methods such as modeling mass flows analysis (MFA), Life cycle analysis (LCA) and semi-quantitative methods for decision support for sustainable innovation. PhD Student in Safe and Sustainable Green
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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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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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plumes from point sources using the MicroHH atmospheric model. Analysis of plume dynamics and NOx chemistry in the high-resolution simulations. Develop and refine data-driven methods for emission
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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
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associated with treating bone fractures are escalating rapidly. This growing demand underscores the critical need for advanced medical implants that provide effective, long-lasting solutions to support bone
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world