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, collaborating with researchers, policymakers, industry partners, and farmers who all work on translating complex data and modelling results into actionable insights. Key responsibilities Develop and apply
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turbulence profiles provide additional data for training and testing. A specific aim is to further develop the methodology of modular compositional learning (MCL). Here, an aquatic ecosystem model is
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. The postdoctoral researcher will collaborate closely with an engineering team responsible for process integration and prototype development Expected start date and duration of employment This is a 2.5–year position
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. The postdoctoral researcher will collaborate closely with an engineering team responsible for process integration and prototype development Expected start date and duration of employment This is a 2.5–year position
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Aarhus University is seeking two postdoctoral fellows for the Novo Nordisk Foundation CO2 Researc...
of defined microbial communities, and modeling on metabolic interactions. Your qualifications You should have a PhD in microbiology, molecular biology, molecular genetics, or related field, and
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operation of defined microbial communities, and modeling on metabolic interactions. Your qualifications You should have a PhD in microbiology, molecular biology, molecular genetics, or related field, and
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LiDAR-based ecology. We're looking for candidates with strong technical skills and ecological interest—people who want to use LiDAR, AI, and spatial modeling to advance our understanding of vegetation
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the composition of organic components, the moisture binding ability and the embedded mycobiome of the materials. Your main responsibilities will include, but are not limited to: Mapping fungal presence in biobased
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Job Description We are seeking an ambitious and dedicated researcher for a 2-year postdoctoral position in the field of microbial metabolism and experimental animal models for cardiometabolic
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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