227 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions in Switzerland
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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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. • 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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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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Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi
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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