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Field
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distributed data processing approaches to address issues of incomplete or redundant multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will
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substantial time with research partners, both FHI in Oslo, Norway, and Norkart in Kristiansand, Norway. The department has three large and active research groups in Information and Communication Technology
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Familiarity with large-scale survey data, conflict and political violence event data, and administrative datasets Strong analytical skills and ability to communicate clearly Ability to work both independently
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at the local level, within households, communities, and local politics. The project combines large-scale surveys, survey experiments, administrative and archival data, GIS, and qualitative field research in
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dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and working with large ensembles
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://mountainsinmotion.w.uib.no/ ), but there is also flexibility for the candidate to incorporate additional field data. This PhD project offers a great opportunity to work with large-scale biodiversity and climate datasets
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modelling, data-driven climate modelling, and working with large ensembles of climate/weather model output are advantages. The LEAD AI mobility rules must be followed. Outgoing fellowships require
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tools along with efficient handling of big data with high time-series resolution. To improve the efficiency in secure power supply through operations and monitoring, Statnett has installed a prototype of
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perspective, DPPs represent a large-scale, dynamic, and heterogeneous data integration problem, where challenges include missing or noisy data, redundancy across sources, dynamic updates, and the need
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