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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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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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AI models into complex industrial and urban environments. Expertise in designing distributed architectures for IoT, edge, and cloud. Proficiency in handling large-scale data and optimizing information
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to incorporate additional field data. This PhD project offers a great opportunity to work with large-scale biodiversity and climate datasets, develop strong analytical skills, and collaborate with international
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project at CERN, funded by the Research Council of Norway. The experiments at the Large Hadron Collider (LHC) at CERN are currently collecting data until 2026, after which the accelerator and experiments
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with register data or large datasets will be considered an advantage. Experience with, or knowledge of, Nordic health registries or databases and/or pharmacological epidemiology is desirable. Experience
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of the Learning domain and C-LaBL at large. Contact For further information about the position, please contact Professor Øystein A. Vangsnes: email: oystein.vangsnes@uit.no or Professor Merete Anderssen: email
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STATA, R, or SAS, will be viewed positively. Documented or demonstrated ability to work independently with register data or large datasets will be considered an advantage. Experience with, or knowledge