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analysis of complex, longitudinal, and high-dimensional data (e.g., immunometabolic profiles, clinical data, biomarkers). Development and application of predictive models and algorithms for diagnostics
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. Through excellent research, innovation, and education, we contribute to the development of future industrial value chains. The Division of Product Development conducts research and education in innovative
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opportunity to work at the interface of AI, computational physics, and electrochemistry, within two vibrant research environments (FLOW and Applied Electrochemistry) and to contribute to the development of next
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splicing and computational method development. The Chen lab is interested in developing advanced computational approaches to decipher RNA diversity and its regulatory mechanisms in human metabolic diseases
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the research and education has a unique breadth, with large activities in classical scientific computing areas such as mathematical modeling, development and analysis of algorithms, scientific software
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of support in career development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en
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for three weeks of training in higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity of further development. Detailed
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are expected to conduct data collection with faith-based refugee relief organisations across Europe, combining semi-structured interviews with participant observation. They contribute to the development
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to work on biobased polymer development for the Mission 0 House initiative. This position focuses on experimental research into sustainable production of cellulose fiber, both complementary to and supported
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for efficient quantification of spatial distribution in electron tomograms? Examples of work that you may conduct during your postdoc: Algorithm development and implementation (e.g. in C++). Machine learning