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UiO/Anders Lien 15th August 2025 Languages English English English Postdoctoral Research Fellow in terrestrial carbon cycle modelling Apply for this job See advertisement About the position Position
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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scientists, technicians, modelers, and municipal planners. The project draws on phenomenological perspectives, including post-phenomenology, but relevant perspectives also include science and technology
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“greenhouse” (warmer than present) conditions. In i2B we will retrieve new, key Arctic geological archives of past warmth and employ climate models to bring our current knowledge about a warm Arctic beyond the
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on childhood dementia CLN3, as part of ongoing research in the Bjørås group . About the project The current project is aiming at developing novel human models for childhood dementia CLN3 to recapitulate disease
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of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian
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scientists, technicians, modelers, and municipal planners. The project draws on phenomenological perspectives, including post-phenomenology, but relevant perspectives also include science and technology
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plus. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained at master's level. Master students can apply, but the master's degree must be obtained and
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correspond to a five-year Norwegian course, where 120 credits have been obtained at master's level. Master students can apply, but the master's degree must be obtained and documented before starting
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All