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socioeconomic inequality. DIVREP involves three broad objectives: (1) assembling data on childbearing age and fertility levels, and estimating novel indicators of variation in reproduction for over 120 countries
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of variation in reproduction for over 120 countries; (2) using these indicators to examine global patterns and long-term trends in disparities in reproductive behaviour within countries; (3) studying micro- and
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-users and stakeholders to help prepare for climate-related impacts of likely future variations in the jet stream. JetVar involves close collaboration with Co-Investigators and Partners at the Universities
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development and pro and antisocial behaviours. We believe it is essential to investigate the determinants of typical development in order to understand variation in behaviour (or illness). About the role We
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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supporting statement and CV. Your supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience. Please restrict your documentation
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with the global mid-ocean ridge system, shape surface volcanism and associated ecosystems. The Galápagos Islands provide an archetypal setting for investigating these processes. By exploring plume-ridge
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, the researcher’s role will centre predominantly on the Adriatic area, carrying out a phonetic exploration of variation and contact between Italo-Romance varieties and Croatian. They will also liaise with other
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climate scientists and artificial intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine