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models and automation. We offer In this role, you will: Have the freedom to develop your research ideas and skills. Have the opportunity to collaborate internally with other ETH faculty as
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modeling. Transcriptome recording and cellular history reconstruction We are advancing our CRISPR-based transcriptional recording method (Schmidt, Nature, 2018; Tanna, Nature Protocols, 2020) that encodes
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, analytical modelling, and numerical simulations to develop and validate novel reinforcement systems. This position is part of an Innosuisse-funded collaborative project with industry partners, focusing
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conventional methods. By combining advanced experiments with collaborations in theory and modeling, our research aims not only to deepen the fundamental understanding of ferroic systems, but also to open new
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the technical implementation, the candidate will contribute to the development of regulatory frameworks, business models, and social analyses that enable broader adoption of community PV. This interdisciplinary
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responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling, or generative modeling. Collaborating with
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plasma models (such as magnetized Vlasov–Poisson and Vlasov–Maxwell systems). - The position includes teaching responsibilities, such as organising a course and assisting with exam grading
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closely with our theory collaborators to model and explain the observed results.
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characterization. Project background The highly interdisciplinary project is about the development, characterization, modeling, and application of micro-and nanoscale ultrasound contrast agents for molecular imaging