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. Our research groups work with the development of computer systems for data analysis, development of machine learning methods, and the clinical use of technology. Within the research group you will
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position within the research project “Statistical Methods for Online Detection of Anomalies” (SODA), newly funded by the Norwegian Research Council and affiliated with Integreat – the Norwegian Centre
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development of computer systems for data analysis, development of machine learning methods, and the clinical use of technology. Within the research groups you will therefore work together with computer
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for simulations, we aim to explore solution strategies to calculate the amount of water given meteorological data and map data. Here, in addition to traditional discretization methods such as finite elements and
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how people estimate quantities. The project combines theoretical modeling with behavioral experiments to advance our understanding of the cognitive processes that connect our sense of magnitude with
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(IRT) models in small samples. The ideal candidate has prior knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant
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mixed methods, including interviews, participant observations, document studies, quantitative approaches and social interventions. The candidate must be familiar with some of these approaches. • Good oral
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teaching Experience with water infrastructure modelling with a focus on computational methods A strong link to the topic interdependence and cascading effects in Urban infrastructure Systems. A strong
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knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant computing language. Experience with machine learning methods is a
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, and AI chatbot chats; (b) quantitative content analysis; (c)text mining and machine learning methods; (d) survey design and public opinion research; (e) election studies; (f) the Norwegian political