29 combustion-modelling-postdoc Postdoctoral positions at Aarhus University in Denmark
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), Aarhus University we are executing a broad portfolio of projects within electrochemical energy conversion and storage technologies. Applications are invited for a 2 year postdoc position in the field
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The Department of Food Science, Aarhus University (Denmark), invites applications for a 36-month postdoc position to work the physical chemistry of food proteins, in particular their structure
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on climate impact research and process-based modelling of biogeochemistry, agronomy, biology and geography from Aarhus University and University of Copenhagen, as well as international partners. Field
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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October or as soon as possible thereafter. Jobdescription This postdoc position offers a unique opportunity to bridge environmental modelling, behavioral insight, and policy innovation to understand and
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parental leave, subsidised childcare, and access to excellent public healthcare and education. As a postdoc at Aarhus University, you will benefit from a supportive and flexible workplace culture that values
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Center for Music in the Brain, Department of Clinical Medicine at Faculty of Health at Aarhus University invites applications for a position as Postdoc in the field of Neuroscience as per 1 October
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University also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here . The application must be submitted via Aarhus University’s
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-Orbitrap-HRMS (for neutral PFAS), HPLC-Orbitrap-HRMS and HPLC-QToF-HRMS (for non-target screening of ionic PFAS) and Combustion Ion Chromatograph (CIC) for PFAS Total, such as Total Fluorine (TF) and
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computational models to map co-expression networks and predict systemic disease transitions. Characterise intestinal microbiome changes and their correlation with inflammatory diseases. Computational modelling