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ETH Zürich’s Nash Lab in Basel seeks a Doctoral Student to develop high-throughput assays for protein interactions relevant to blood clotting and to apply sequencing data and ML to model mechanics. You will join two departments and work across experimental and computational biology, with hands-on training from design to predictive modeling.
The role includes collaboration with a DBSSE group, enrollment in the Basel doctoral program, and teaching involvement, all within an interdisciplinary,
The Nash Lab (Lab for Engineering Synthetic Systems) is jointly affiliated with the Department of Chemistry at the University of Basel and the Department of Biosystems Science and Engineering of ETH Zurich (located in Basel). We work at the interface of protein engineering, molecular biophysics, and synthetic biology, developing new experimental and computational approaches to understand and engineer proteins for useful biomedical and industrial applications. Our research combines high-throughput screening, directed evolution, and single-molecule biophysical methods, and spans projects from fundamental protein science to translational applications.
Many proteins in the human body experience shear stress and other types of mechanical force, and this is believed to play a central role in their activity. Yet we still lack high-throughput techniques to measure how proteins respond to mechanical force. This project will develop high-throughput methods involving molecular surface display and deep sequencing to study force-dependent behavior in protein systems. These datasets will, in turn, be used to train machine learning models capable of predicting mechanical behaviors of molecular and cellular systems.
As a proof of concept, we will apply this platform to study interactions central to blood clotting, with direct relevance to Von Willebrand disease, a common bleeding disorder. The Doctoral Student will work at the interface of protein engineering, single-molecule biophysics, and machine learning, in close collaboration with a computational biology group (Beerenwinkel) at D-BSSE. The project offers hands-on training across the full pipeline, from high-throughput experimental design to predictive modeling, and the resulting methods and datasets are intended to serve as an open resource for the broader research community.
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