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. We envisage combining process-based modelling and machine learning, as well as integration of Earth observation data into the modelling framework. The objective is to quantify the GHG budgets
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information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders/eksterne-personer-en/research-leaders-2026/niels-van-berkel/. Your competencies You hold, or will
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consist of the PI, one postdoc and one PhD. Read more about the project here: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders/eksterne-personer-en/research-leaders-2025/mathias
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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to learning Danish, including reading, writing, and speaking, is expected during the employment period. Contact Further information on the position may be obtained from Professor Margit Bak Jensen
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Postdoctoral position for the project Human AI Collaboration: Imaginaries, Interventions, Interfaces
full project description here, including a detailed description of SPs and WPs: https://pure.au.dk/portal/da/projects/human-ai-collaboration-imaginaries-interventions-interfaces . Information about the
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. You can read more about the Department of Management at: https://mgmt.au.dk/ Further information The Department of Management offers a stimulating international environment. The department conducts
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: Overcoming Inequity in Embodied Learning in Danish Vocational Education and Beyond, funded by Independent Research Fund Denmark. This is a full-time (37 hours per week), fixed-term (24 months) postdoctoral
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply