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the project/work tasks: Advanced data management on large datasets to facilitate data for statistical analyses Design and plan analyses Make efficient analysis pipe-lines in the statistical software R
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School of Public Health i Boston with Associate Professor Jannicke Igland as local principal investigator at the University of Bergen About the project/work tasks: Advanced data management on large
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, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and working with large ensembles of climate/weather model output are advantages
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climate change are far-reaching, particularly when it comes to identifying and interpreting trends in regional-to-local scale signals and extreme events. Large ensembles of climate simulations are a key
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to develop methodologies for real-time modeling and inversion of geophysical well logs, with a particular focus on borehole electromagnetic data during drilling. This includes the further development
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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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game-based community. building and social science communication. Provide administrative and academic support to research groups, such as literature searches, data collection, reference management, and
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primary focus on wind energy. It examines how climate change affects their efficiency and reliability and how large-scale systems impact regional or larger-scale climates. The candidate will collaborate
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their efficiency and reliability and how large-scale systems impact regional or larger-scale climates. The candidate will collaborate with GFI's energy and climate groups on this research. The project aims to assess
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practical experience in data science applied to medical or population genomics or other omic demonstrate experience in analyzing large omic data be proficient in one programming language be able to work