88 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"CESBIO" positions at Aarhus University in Denmark
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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
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with teamwork in a great team Additional information Your place of work will be the AU International Centre at Høegh Guldbergs Gade 4a in Aarhus. The position is paid according to state agreements. Who
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administrative staff, and students. You should also be structured and meticulous in your work, which is essential when collecting and managing research data. Qualifications You are expected to hold a MSc or PhD
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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funding The place of work is Langelandsgade 140, 8000 Aarhus C, and the area of employment is the Department of Chemistry, Aarhus University and related departments. Further information about the position
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An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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to archaeological data and fieldwork as well as Scandinavian Archaeology will be an advantage. Subject to funding, an associate professorship in open competition will be advertised following this three-year period
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researcher network. The department consists of nine research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe
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data. We also offer a great mentoring experience, a collaborative environment in which the candidate will be able to share across subfields and applications, and a research environment characterized by
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on “ Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity ” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data