49 linked-data-"https:"-"https:"-"https:"-"Stanford-University" Postdoctoral positions at Aarhus University
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reference If you want a referee to upload a letter of reference on your behalf, please state the referee’s contact information when you submit your application. We strongly recommend that you make
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Work The place of work is Ny Munkegade 120, 8000 Aarhus C. Contact Information Further information about the position may be obtained from / For further information please contact: Dr Simon Wall +45
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The Department of Digital Design and Information Studies within the School of Communication and Culture at Aarhus University invites applications for a postdoctoral position in practice-based
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: Establish and develop experimental protocols and pipelines and implement data management compliance. Presentation of your work in various meetings (locally at the department, national and international
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information For further information, please contact: Professor Torben Heick Jensen, thj@mbg.au.dk, phone +45 60202705 Application procedure Shortlisting is used. This means that after the deadline
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. For further information, please contact Professor, dr. scient. et techn. Bo Brummerstedt Iversen (bo@chem.au.dk). Applications including CV, full publication list, references and description of qualifications
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University with related departments. Contact information For further information, please contact Prof Kim Daasbjerg at +45 23 48 52 49 or kdaa@chem.au.dk or alternatively Associate Professor Behzad Partoon
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execute MR experiments using hyperpolarized 13C tracers in collaboration with project partners Develop robust experimental procedures and protocols to ensure reproducibility Contribute to data analysis and
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, which is a collaboration between the Department of Business Development and Technology and the Department of Digital Design and Information Studies. The project ‘Practice Resonant AI Ethics for the Public
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main areas of work: Exploration of heterogeneity in GDM risk and GDM subtypes and application of these insights to develop a GDM risk prediction model, based on data from The Danish Blood Donor Study