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international research network and good industrial and professional collaboration. Please refer to Department of Animal and Veterinary Sciences (au.dk) for further information about the department: https
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Postdoctoral position for the project Human AI Collaboration: Imaginaries, Interventions, Interfaces
development programme targeted at career development for postdocs at AU. You can read more about it here: https://talent.au.dk/junior-researcher-development-programme/ If nothing else is noted, applications
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development Qualifications PhD degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages
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WRF and WRF-Chem • You will be working in an aerobiological research group funded by the Novo Nordisk Foundation containing two additional post docs, a PhD student and a professor. This teams covers
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: http://international.au.dk/research/ Aarhus University also offers a junior researcher development programme targeted at career development for postdocs at AU. You can read more about it here: https
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a PhD in biology, earth system or data science, or a similar field, and have several years of experience with interdisciplinary collaborations focused on understanding biodiversity dynamics by
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and supervision of students at the bachelor's, master's, and PhD levels. Qualifications for the postdoctoral position Academic qualifications at PhD level in animal or veterinary sciences. Research
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. You will attend local and international scientific conferences. Your profile Applicants should hold a PhD in neurobiology, cell biology or related fields. You should be experienced in cellular
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contributing to the continued expansion of a proteomics research programme at Aarhus University. Your profile Applicants should hold a PhD in proteomics, analytical chemistry, molecular biology, biochemistry
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factor analysis. Develop robust, shareable bioinformatics pipelines for epidemio-genomic and phylogenomic analyses using genomic & metagenomic datasets and implement them in high performance computing