932 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Humboldt Universität zu Berlin" positions in Sweden
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to act as references. A brief research statement (1-4 pages) outlining your research interests, including previous work and independent research plans. Applications to be made at https://web103
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mainly consists of writing a doctoral thesis. You will be part of a research project but also part of a larger researcher group (Molecular Ecology & Evolution Lab). You will also participate in doctoral
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molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection. For more information, please see www.scilifelab.se/data-driven . During 2022-2024
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system developer for our national DDLS data services, with a profile towards data engineering, systems development, and data management. In this role, you are expected to develop software, systems, and
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the same roof. History of Science and Ideas studies how ways of thinking, as well as the development of sciences and knowledge, change over time and how these processes both shape and are shaped by societies
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development as well as patent and innovation support. Where to apply Website https://uu.varbi.com/en/what:job/jobID:893222/type:job/where:39/apply:1 Requirements Research FieldChemistryEducation LevelPhD
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research within Unmanned Traffic Management (UTM). In this role, your primary responsibility will be the hands-on development of advanced simulations and prototypes that help us test and validate new UTM
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collaboration meet. The research topics in the department span fundamental and applied research to contribute to the development of a sustainable society. We are Sweden's largest mathematical department, with
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification