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are expected to be the CO2 capture technology for early large-scale deployment. A detailed understanding of the CO2 capture solvent is therefore necessary to ensure development and application of an optimal
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or in the face of adversity show curiosity and a strong motivation for the subject analyze data, assess different perspectives and draw well-founded conclusions be flexible and open to adjusting
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- to large-scale ocean biogeochemistry, in particular of carbon cycle processes, dynamics of oxygen and nutrient cycles, is required. Expertise in scientific scripting, programming, and data analysis (e.g
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to calculate your points for admission. Emphasis is also placed on your: background in algebraic or symplectic geometry or mathematical physics programming skills and experience with computer algebra packages
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variability and predictability is an advantage. Experience with Linux clusters, and running Earth System Models, is an advantage. Experience with handling large datasets, such as CMIP data, is an advantage
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. Potential methodological topics focus on meta-analyses and the analysis of large-scale assessment data: Methods and approaches to synthesize large data sets via meta-analyses (e.g., meta-analyses of large
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studies: How AI can be used to prioritize risk areas within supervision activities based on existing data held by supervisory authorities. How the conduct of supervision can be supported by AI, including
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computing technologies in medical contexts. This could include issues such as equity/discrimination, data ownership, unexpected implications of the AI Act for mental healthcare, and other issues of relevance
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, sedimentology Competence desired: Chemistry, geochemistry, geosciences, modelling 4) Proxy models for CO₂ sequestration This project will develop data-driven proxy models for CO₂-sequestration forecasting in