17 structural-engineering-"https:"-"https:"-"https:"-"https:"-"Simons-Foundation" positions at Umeå University
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position aimed at the development of a novel organic-electronics technology, termed a light-emitting electrochemical cell (LEC). The LEC is currently attracting increasing interest from both academia and
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Summary The announcement concerns a temporary position as a Research Engineer at the Department of Chemistry
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who participate in courses and activities within the research school at Umeå University, Luleå University of Technology, Chalmers University of Technology, and the synchrotron MAX IV Laboratory at Lund
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initially symmetrical structures transform into complex and specialized forms. This process is essential for the development of multicellular organisms and plays a key role in shaping the remarkable diversity
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brain imaging methods as a window into human brain plasticity. Projects will combine new data acquisition with analysis of existing datasets, and may leverage: MRI (structural, diffusion, and functional
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investigate how public agencies adopt and integrate AI technologies, what forms of automation and augmentation emerge, and how these developments influence organizational structures, professional roles, and
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construction. The position is 20% part-time with the start date in December 2025. The application deadline is 13 November, 2025. Job description As teaching assistant, you will assist UMA researchers in
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to work in a structured and methodical manner Eligibility A person who has been awarded a doctorate in a subject relevant to the position, or a foreign qualification deemed to be the equivalent of a
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which can be exploited by malicious actors. This project will be conducted in the Software Engineering and Security (SES) group. The student will conduct an in-depth investigation of the software patching
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce