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-prediction benchmark studies. Depending on the qualifications and preferences of the candidate, the work may entail experimental investigations and/or modelling in the open-source computational fluid dynamics
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interdisciplinary research group with both experimental activities and numerical modelling. The PhD candidate’s tasks will primarily be to model and simulate CO2 hydrate sealing in sediments but also to take part in
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capabilities of consequence models through blind-prediction benchmark studies. Depending on the qualifications and preferences of the candidate, the work may entail experimental investigations and/or modelling
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employment that the PhD Research Fellow is enrolled in USN’s PhD-program in PhD in Technology within three months of accession in the position. It may be possible to get a four-year full-time period consisting
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oppdatert 08.08.2025 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde skadelige programmer eller
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sediment volumes, deposition rates and transport pathways through time, combined with onshore exhumation data and topo/landscape models Evaluation of thermocronometric data and provenance data to establish a
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teaching Experience with water infrastructure modelling with a focus on computational methods A strong link to the topic interdependence and cascading effects in Urban infrastructure Systems. A strong
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candidate will work in an interdisciplinary research group with both experimental activities and numerical modelling. The PhD candidate’s tasks will primarily be experimental, to conduct hydrate sealing
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6th June 2025 Languages English English Norsk Nynorsk English PhD Research Fellow in energy resources and system modeling Apply for this job See advertisement UiB - Knowledge that shapes society
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, researcher training, PhD summer schools, frequent symposia and exhibitions. About the PhD project and work tasks The successful candidate’s PhD project will investigate narrative through computational models