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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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the Programme and provide their expertise and support to a large multidisciplinary team of chemists, radiochemists, (radio)biologists, pharmacists, and clinicians. The successful candidate will have extensive in
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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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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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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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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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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
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and applying genetic and genomic approaches to biodiversity research. This includes integrating environmental DNA (eDNA) and molecular tools with ecological data to enhance our ability to assess