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computational fluid dynamics, quantum computing and high-performance scientific computing, and to engage with researchers from both academia and industry. You will have substantial scientific independence
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bring extensive experience in high-performance computing and AI-driven scientific research. Together, we aim to push the boundaries of what AI can achieve in scientific domains, ensuring that
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calculations will be performed using ETH Zurich’s high-performance computing infrastructure. In addition to research, the doctoral candidate will contribute to general laboratory activities and will have
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pharmacokinetic properties, including rapid blood clearance and high kidney uptake. This project aims to investigate how defined N-glycosylation can improve the biological and pharmacological properties of
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-surface infrastructure Advanced Computing: Competency in compiled languages (such as C++, Rust, or Julia) and multi-physics co-simulation standards (e.g., FMI/FMUs) and Experience in collaborative
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essential for translating LIFE's science goals into science requirements and corresponding instrument-performance requirements. Our group has developed the atmospheric retrieval framework pyRetLIFE and
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constraints on the evolution of early Earth Coupling biological and abiotic models or working with Earth-system feedbacks High-performance computing and large-scale numerical simulations Experience working
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facilities for optical and high-speed imaging, material preparation, and scientific computing The position offers substantial freedom to develop independent research directions while collaborating closely
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interior or structure modelling, thermal evolution, high-pressure equations of state, atmospheric modelling, magma-ocean chemistry, or statistical inference Independence, curiosity, and an interest
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platform , published in Nature Communications (https://www.nature.com/articles/s41467-020-18059-7), that enables localized, hyper-efficient delivery of therapeutic compounds to specific brain regions