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into downstream probabilistic time-to-event models. Applications will focus on prostate cancer, using large-scale clinical registry data and unstructured medical text. The project is interdisciplinary and will
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preferential viewing behavior, using large-scale electrophysiology, behavioral experiments, and computational modeling. We welcome applications from recent PhD graduates who are interested in these or related
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data protection information. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance
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on the design of AI-based surrogates of large-scale energy system models and will be integrated in the activities of the Nexus-e group with close links to the ETH AI Center and the Reliability and Risk
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derive generally applicable guidelines for the design of improved organic materials. Interested candidates may want to take a look at our recent work https://www.nature.com/articles/s41467-025-67722-4 and
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formation of large-scale structures Active knowledge of spoken and written English Specific Requirements In addition to the application document, applicant needs to provide: Curriculum Vitae (CV), Diploma, e
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Faculty. Excellent scientific achievements are required, including experience in conducting large scale clinical trials, translational research, and acquiring third-party funding, alongside a strong
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digital platforms. You will bring: Extensive experience managing large-scale Microsoft Entra ID and Active Directory environments Experience with IAM lifecycle management and identity governance processes
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on an industry innovation research project where you will be part of the research team to develop a demonstration scale low pressure membrane system in collaboration with Atera Water Pte Ltd. Key Responsibilities
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media where the large proportion of interfaces and the finite amount of matter lead to remarkable properties. The systems studied share the common feature of exhibiting heterogeneities at the nanometric