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active sites), in vitro and in vivo enzyme screenings, electrochemistry, and machine learning-assisted directed evolution. As part of this project, you will collaborate closely with PhD students and
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applications, including solving mathematical reasoning problems and tackling the Abstraction and Reasoning Corpus (ARC) challenge among others. The ideal candidate has a strong background in machine learning and
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journals. The position offers close supervision and collaboration with leading faculty and senior researchers, as well as access to ETH Zurich’s excellent research infrastructure and strong international
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-edge research with the potential for high impact in the field of environmental microbiology Encouraging and collaborative research environment Cutting-edge computational infrastructure Support for career
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. engineer new nanopores (solid-state or biological). collaborate within an interdisciplinary team of biophysicists, nanotechnologists, and biochemists. present your findings at conferences and in high-impact
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kinetic information. Evaluate bioconjugation modalities and probes for DNA/RNA and protein systems. Investigate mechanistic effects of biomolecules that were inaccessible previously. Collaborate within
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looking for new challenges and you are eager to learn new methods You are a highly motivated team player with good communication skills Experience in some of the following fields will be of advantage
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opportunity to learn, develop and apply a range of cutting-edge modeling and computational techniques. You will work in an interdisciplinary, cutting-edge, fast-paced research environment, interact with