316 evolution "https:" "https:" "https:" "https:" "https:" "U.S" "St" "St" research jobs in Sweden
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it effect engagement and learning. For more information about the Akelius Math Learning Lab, see: https://www.chalmers.se/institutioner/mv/akelius-math-learning-lab/ Who we are looking
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Website https://umustipendie.varbi.com/en/what:job/jobID:887298/type:job/where:39/apply… Requirements Research FieldMedical sciencesEducation LevelPhD or equivalent Research FieldMedical sciencesYears
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investigations of insect sensory systems. Assessment criteria and other qualifications This is a career development position primarily focused on research. The position is intended as an initial step in a career
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. The objective is to advance the method from a research concept to large-scale implementation in urban environments, supporting proactive and data-driven bridge management. The work includes system development and
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such as the heart and nervous system, invariably with fatal consequences. The project will focus on probing the molecular factors underlying this instability (see for example https://www.nature.com/articles
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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-emitting devices and other advanced optoelectronic applications. The positions are based in the Green Nanodots Group (https://www.umu.se/en/research/groups/green-nanodots/ ), Department of Physics, Umeå
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- for technical and societal development. Our cross-disciplinary approach gives interesting collaborations in academy, industry and society, and is a driving force for innovations, results and breakthroughs. We
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This project 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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, etc.) development of predictive models and digital decision-support tools for nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty