44 postdoc-structural-engineering Postdoctoral research jobs at Aarhus University in Denmark
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Mathematics. The positions have 1st September 2026 as earliest possible start dates. There are postdoc positions available in the areas listed here: https://math.au.dk/en/about/vacancies/postdoc/ When applying
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and Production Engineering invites you to apply for a 20-month postdoc position. Expected start date and duration of employment This is a 20-month position from May 1st 2026 or as soon possible. Job
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at Danish Universities under the Danish Ministry of Science, Technology and Innovation . The application must be in English and include a curriculum vitae, degree certificate, a complete list of publications
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the ongoing EU Horizon Europe projects. Expected start date and duration of employment These are 2‑year postdoc positions, with a preferred start on 1 June 2026 or as soon as possible thereafter. Job
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protein engineering, characterization of protein interactions by various methods, de novo design of protein binders, integrative structural biology using NMR, SAXS and/or single molecule FRET. Your profile
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. Supporting the development of structured collaborations between education providers and industry to strengthen training relevance and uptake. Communication, dissemination, and strategic outputs Coordinating
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The Department of Ecoscience, Aarhus University, invites applications for a full-time postdoc for a period of 1.5 years. Expected start date and duration of employment This is a full-time position
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The Department of Ecoscience, Section for Wildlife Ecology at Aarhus University is seeking a postdoc (2 years) to develop and apply camera-based methods for population monitoring of bats
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for fundamental optomechanics and practical gas sensing applications. The postdoctoral fellow is expected to contribute to the design, fabrication and optical/structural/mechanical characterization of “string
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater