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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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of smart technologies to visualize yard operations in a digital form (such as virtual models and digital twins). Smart technologies can collect, analyze, and represent data from various sources
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, key Arctic geological archives of past warmth and employ climate models to bring our current knowledge about a warm Arctic beyond the state-of-the-art. The major strength and aim of i2B
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both water tanks and with phase change material - PCM). Digital twins of the system for real-time decisions, based on petroleum field experience. LCA and economic conciderations. Modeling and reservoir
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access. The goals of such access include supporting registry operations as well as health care research. Of particular interest in this context are differentially private algorithms for statistical model
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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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and concepts with existing seismic models. The project will involve collaboration with industry partners and other scientific teams. The candidate will work alongside geoscientists in the BASINS section
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equivalent. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained at master's level. You must have a strong academic background from your previous studies
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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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models, aiming to reduce CO₂ emissions and improve resource efficiency through enhanced data-driven lifecycle management. A DPP can be viewed as a structured, machine-readable knowledge artifact