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understanding of the aging of solid insulation under mixed-frequency medium-voltage stress, see https://doi.org/10.1088/1361-6463/acd55f for a relevant example research work of our team in this area. Profile
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) Contribute to the strategic direction of research Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with
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& Machine Learning: Experience in deploying machine learning models and data science workflows in a research context (e.g., cheminformatics, predictive modelling). Design of Experiments (DoE): Knowledge
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methods and approaches are needed to better tackle the challenges posed by increased uncertainty and complexity. Machine learning (ML) and artificial intelligence (AI) methods have shown promise
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Systems.”Funded through an ETH Zurich Career Seed Award, this project aims to develop scientific machine learning frameworks that integrate physics-based modeling with neural network architectures. The goal
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1855, ETH Zurich today has more than 18,500 students from over 110 countries, including 4,000 doctoral students. About 500 professors currently teach and conduct research in engineering, architecture
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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of cutting-edge tools, models, and strategies to understand and engineer immune systems for translational medicine. Candidates may use integrative approaches that combine immunogenomics, machine learning
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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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dynamical systems, and machine learning, with applications to synthetic biology and biomolecular circuit design. Our research develops mathematical and computational frameworks for understanding and