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. This is enabled by an extensive farm-based infrastructure with large scale measurement of methane emissions and feed intake on individual cows, as well as collaborations with industry and international
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together a large network of European stakeholders across universities, industry, training providers, and policy organisations, with the goal of strengthening Europe’s quantum talent pipeline and accelerating
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, conducting interviews, analyzing interview transcripts, and writing academic articles based on the data. YOPOW seeks to deliver cross-national evidence across the three work packages, with work package II
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datasets, UAV-derived information on NBS, and environmental data to quantify ecosystem services across Danish agricultural landscapes. The postdoc will work within an interdisciplinary team of applied and
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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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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hold a PhD in oceanography, marine ecology, computer sciences, data sciences or similar. We expect that you have: Expert knowledge on network modelling, especially aimed at ecological applications Strong
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the integration of spatial transcriptomic and metabolomic data. You will report to the Professor Ian Mills/Professor Per Qvist. Your competences You have academic qualifications at PhD level, for example within
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the department We seek a candidate with the following qualifications : Required: PhD in wildlife ecology, conservation biology, or a related field Experience with technology-assisted wildlife monitoring (e.g
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” funded by Independent Research Fund Denmark and led by Associate Professor Christoffer Basse Eriksen. The project aims to carry out the first large-scale study of the making of the Flora Danica (1761–1883