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research in deep learning models for multi-sensor satellite data (e.g. SAR, SMAP) within a large international research project on AI-driven solutions for groundwater management. Expected start date and
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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
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., camera traps, thermal imaging, acoustic sensors) Practical skills in programming and analysis of large datasets Publication record in relevant areas Ability to communicate effectively in English, both
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
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be developed and implemented in the GEOS-Chem chemical transport model, coupled to the Community Earth System Model. Standardized large wildfire events will be simulated based on historical data and
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plant growth. We are particularly interested in deciphering the role of the large intrinsically disordered loops using structural and biophysical approaches. The project may involve a combination of
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research group "AI and big data in Radiation Oncology" (read more about the group here: https://www.en.auh.dk/departments/the-danish-centre-for-particle-therapy/research/research-groups/artificial
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characterized by more favorable citizen attitudes. The project develops new theoretical concepts and empirical measures, using a novel sequential research design that integrates observational data, large-scale
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quality and functioning, particularly in plumes near river outlets. This post doc project will rely on existing data as well as new field data of nutrients, carbon, and stable isotopes from riverine-coast
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media data using platform APIs and/or web scraping analyse large-scale data using appropriate digital methods (flexible, but please specify) present research at national and/or international events lead