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. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the
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, collaborating across disciplines to tackle fundamental challenges through innovative methods, theory and critical analysis. The fellowship period is 3 years. Starting date as soon as possible and upon individual
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of measurement competence through teaching and outreach. UiO/ Anders Lien via Unsplash UiO/ Anders Lien More about the position The position is tied to CREATE’s strand 3. Strand 3 aims to develop novel methods and
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
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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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. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining
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Skills in advanced computational methods (e.g., C++) Jarli og Jordan/UiO via Unsplash Jarli og Jordan/UiO Personal skills Teamwork skills, as well as the ability to work independently Enthusiasm, personal
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modification by the small ubiquitin-like modifier SUMO. At a systems level, we found that SUMOylation of chromatin-bound proteins ensures the precision of gene expression and transcription factor dynamics during
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UiO/ Anders Lien More about the position The position is tied to CREATE’s strand 3. Strand 3 aims to develop novel methods and statistical software that are tailor-made to the research questions and
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-of-the-art research methods for drawing causal inferences from non-experimental data. The successful candidate should have prior knowledge of quasi-experimental methods and, preferably, large data sources