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microbial metabolites and its effect on chronic kidney disease and cardiovascular complications, using an in vivo model of chronic kidney disease. Responsibilities and qualifications As a PhD student, you
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computational and experimental approaches, leveraging the large-scale Microflora Danica dataset combined with samples taken from sites undergoing restoration. You will help conduct sampling, refine and apply
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are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate change. Engineering and applied physics
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Job Description These days, the inner workings of molecules and materials can be probed and modelled by advanced simulation tools on modern computer architectures. However, the routine applications
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Genetic Improvement of Dairy Cow Longevity, Using Large-Scale Body Weight Data from an AI-Camera ...
Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is
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/multiqubit . The project is supported by an ERC Consolidator grant (€ 2.6 million) from the European Research Council (EU). Our research aims at exploring quantum information science at the nanoscale and
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are part of a sub-project on Algorithmic Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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24 Aug 2025 Job Information Organisation/Company Aalborg Universitet Department The Technical Faculty of IT and Design, Department of Computer Science, Human-Centered Computing Research Field
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domains. The scientific outcomes are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate