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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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the project/work tasks: The overall goal of this PhD project is to develop methodologies for real-time modeling and inversion of geophysical well logs, with a particular focus on borehole
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event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components. About the LEAD AI fellowship programme LEAD AI is the University
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tool in such applications, but the computational cost of producing ensembles at high enough resolution and with enough members to adequately sample internal variability and capture rare events is a
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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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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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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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. Addressing housing-related health risks in the USA, Vietnam, Turkey, and Ecuador, the project integrates community engagement, data science, and computational modeling. The key objectives of ComDisp
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computational modeling. The key objectives of ComDisp are: • Identifying and understanding housing, air quality, and respiratory health issues in each case study. • Linking climate change models to housing
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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