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The Department of Environmental Science at Aarhus University (AU-ENVS), Roskilde, invites applications for a 2-year position as postdoc in satellite remote sensing and land-use modeling of grassland
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Aarhus BSS Graduate School, Aarhus University invites applicants for two three-year PhD scholarships within the research project “Decoding Danish Firm Innovativeness from the Consumer Perspective
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
pests, or high-throughput phenotyping Solid background in mathematics and scientific programming (R, Python, etc.) along with effective logical reasoning skills Experience with high-performance computing
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pests, or high-throughput phenotyping Solid background in mathematics and scientific programming (R, Python, etc.) along with effective logical reasoning skills Experience with high-performance computing
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cytokines and acute phase reactants. A solid background in assessing the bioavailability of different sources of vitamin D. Demonstrated ability to conduct independent research within this area, experience in
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, MSc and PhD students – though teaching duties may be low The ideal candidates: Holds a PhD with a solid background in quantitative genetics and/or animal breeding Holds a PhD with a solid background in
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from observations, sensors, or eDNA, in combination with environmental drivers, land-use history, and management. Experience with large and heterogeneous datasets is important. The successful candidate
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Master’s degree can apply for a 4 years integrated PhD stipend. The Integrated PhD stipends are only open for appointment with starting date 1 September 2026. According to the most recent US News and World
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other things, pesticides, as well as other projects The ideal candidate is expected to have solid chemical knowledge and experience, including experience with analytical chemical methods. Familiarity with
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under uncertainty (e.g., planning, reinforcement learning, probabilistic reasoning); Multimodal information fusion and state estimation; Foundational or representation learning models for robot