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. Experience working with petrographic thin-section and identifying primary carbonate components and secondary cements. All candidates and projects will have to undergo a check versus national export, sanctions
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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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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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work upon. Suggested reading to explore this line of research further: Kitto, K., Hicks, B., & Buckingham Shum, S. (2023). Using causal models to bridge the divide between big data and educational theory
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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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, 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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“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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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
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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