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for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological, and environmental factors contribute to severe mental and neuropsychiatric disorders
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for giga-scale CO2 storage. Concretely, this PhD project will look at the theoretical foundation and associated numerical and computational methods for large scale poroelastic response on the basin scale
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- to large-scale ocean biogeochemistry, in particular of carbon cycle processes, dynamics of oxygen and nutrient cycles, is required. Expertise in scientific scripting, programming, and data analysis (e.g
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variability and predictability is an advantage. Experience with Linux clusters, and running Earth System Models, is an advantage. Experience with handling large datasets, such as CMIP data, is an advantage
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. Potential methodological topics focus on meta-analyses and the analysis of large-scale assessment data: Methods and approaches to synthesize large data sets via meta-analyses (e.g., meta-analyses of large
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data from aircraft field campaigns and satellites. We are now looking for an engaged and highly qualified PhD candidate to join our collaborative team. As a PhD in the Meteorology group
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. The project will make use of novel and established water tracing diagnostics, including stable water isotopes. There is also a possibility to work with observational data from aircraft field campaigns and
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computational methods for large scale poroelastic response on the basin scale - thus covering multiple CO2 storage locations. This may involve mathematical topics such as upscaling, homogenization and numerical
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is a large national center gathering all research and industry partners in Norway who are focused on batteries. Responsibilities Advances in battery technology are driving innovation in industries
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4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
next generation neural networks for advanced analysis of image and multimodal data. Central research challenges are to develop neural networks that learn more efficiently from limited data