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, with strong expertise in RNA biology, structural biology, enzymology, glycobiology and quantitative proteomics. More information: https://mbg.au.dk What we offer We offer: The opportunity to work in an
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Postdoc in pharmacoepidemiology: Long-term safety and benefits of ADHD medication in children and...
deadline March 22, 2026, at 23.59 hrs. (CET). Apply online https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/jobs/preview/3684/?lastSelectedFacet=CATEGORIES
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-species interaction in gas–liquid bioreactor platforms. Deliver scientific excellence through high-impact publications, dissemination and outreach. Teach and develop courses at BSc and MSc levels in
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The Daasbjerg research group at the Department of Chemistry, Aarhus University, is seeking a candidate for a 31-month postdoctoral position. This position focuses on AI/machine learning to develop a
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including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) Applications
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QGG Aarhus University seeks two Postdoctoral researchers in Quantitative Genetics of sustainable ...
tools or functional genomic information or OMICS to improve genomic prediction models. The persons hired will collaborate with industry partners, teach at undergraduate and graduate levels, and supervise
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research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience
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Professor at Aalborg University (profile: https://vbn.aau.dk/da/persons/jajh/ )The project team consists of the principal investigator, two postdoctoral researchers, and a research assistant working closely
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The Daasbjerg research group at the Department of Chemistry, Aarhus University, is seeking a candidate for a 31-month postdoctoral position. This position focuses on AI/machine learning to develop a
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following thematic areas: • AREA 1: Machine learning and AI-driven methods for design, simulation, and optimisation in architectural and construction engineering. • AREA 2: Robotic and additive