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extraction, alignment, QC metrics, drift/batch correction) and reporting. Advance annotation strategies using modern approaches such as spectral/structure fingerprinting, molecular networking, in-silico
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biology. The project aims to uncover how mechanical properties, forces, and physical phenotypes integrate with molecular networks to regulate the function of complex cellular systems across multiple
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conferences. The postdoc will benefit from ETH Zurich’s strong interdisciplinary and global network, including: The Albert Einstein School of Public Policy, The ETH’s Energy Science Center and ETH's World Food
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electrophysiological recordings to track how tumor invasion alters neuronal network activity at single-cell and population levels Apply spatial transcriptomic techniques to map gene expression changes in tumor-connected
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical
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. The opportunity to work with the world-first imaging device tailored to study drift in riverine ecosystems. Access to an extensive network within the field of Ecohydraulics. Scientific independence, visibility and