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under global change requires accurate and consistent soil information at global scale. Current global soil maps are derived using empirical machine learning that often ignores known soil processes
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. Using an innovative multilevel approach, you will move beyond institutional averages to identify where inclusion succeeds, where disparities persist and how universities can create more equitable learning
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are leading internationally in their respective disciplines and provide an exciting and collaborative environment, with room to learn and develop professionally at the frontier of academic research. You will
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strong affinity for language data; solid programming skills (e.g., Python) and experience with machine learning or NLP, ideally including transformer-based models and word embeddings; excellent English
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or the willingness to learn Dutch is considered an advantage, given the clinical component of the project. You are an enthusiastic team player who contributes to a positive and collaborative working environment
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related field; has strong affinity with the study of socio-economic and ethnic inequality; has experience with advanced quantitative methods (or is motivated to learn); ideally has experience with large
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. The research programme addresses the interaction between power, authority, and legitimacy, which it seeks to connect with social movements, protest, and conflict. Please follow the links to learn more