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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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will be responsible for reviewing relevant research, designing and conducting large-scale sensory and consumer studies, analyzing data, and generating insights that contribute to relevant scientific
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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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of large-scale EM data for groundwater mapping. Teaching and training of Ethiopian partners and students in EM methods, data processing workflows, inversion software, and geological interpretation
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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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addition to the large base of basic research, the center has a large number of ongoing industrial projects and partnerships. In the Valero lab, we offer a dynamic, social and interdisciplinary scientific environment with
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, handling, and synthesising big data geospatial data sets from various data sources. Cutting-edge expertise in advanced statistical analyses of large data sets and strong knowledge of programming languages
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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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assessments. Key responsibilities Design and conduct experiments. Operate and maintain gas measurement equipment and flux chambers. Process, analyze, and visualize large data sets using Matlab, R, Python
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further information are invited to contact Associate Professor Niculina Musat (niculina.musat@bio.au.dk). Who we are The successful candidate will be employed by the Department of Biology at Aarhus