80 data "https:" "https:" "https:" "https:" "University of Innsbruck" Postdoctoral research jobs at Aarhus University
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50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and understanding by employing staff who bring unique perspectives to our department
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quality. Your main tasks will consist of: Conducting independent research of high international quality, including publication in leading journals Performing advanced spatial data analysis, including
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scientific journals Research experience in some of the areas of fungal transformation, CRISP/Cas9 modification of fungal genes, analysis of metabarcoding data, and soil microbiology. Additional qualifications
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. The research project consists of studies that will be based on data from the Danish National Birth Cohort and the National Child Health Register. Your job responsibilities As Postdoc in Epidemiology your
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sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and understanding by
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a focus on metabolomic and transcriptomic data. Performing tissue sectioning, matrix coating of the resultant sections, data acquisition using a Bruker TimsTOF MALDI-2 instrument and downstream
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research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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. Consequently, your employment will as of that date be with a department. Contact information For further information, please contact: Assistant Professor Emil Laust Kristoffersen, +45 29271306, emillk
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for international researchers and accompanying families, including assistance with relocation and career counselling to expat partners. Please find more information about the International Staff Office and the range
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will