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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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digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data, laying the foundations of next
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learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior knowledge of IRT models, a basic understanding of common estimation methods, and
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properly. Please turn on JavaScript in your browser and try again. Jarli & Jordan/ UiO 4th April 2025 Languages English English English PhD Research Fellow in cognition in language models at the Center
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data
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system diseases by combining gene engineering and generative machine learning modeling to design gene therapy constructs and validate the constructs in vitro and in vivo. The candidate will work in a
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modeling of narrative, which includes developing new computational models of narrative and re-implementing historical models (also known as “storytelling systems”) so that they can be easily studied and used
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, wearable and nearable sensor data, continuous glucose monitoring data, self-reported data, and multi-omics analysis to develop predictive models for steroid hormone disorders, particularly adrenal
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-reported data, and multi-omics analysis to develop predictive models for steroid hormone disorders, particularly adrenal insufficiency and cortisol dysregulation. The project targets patients with stress
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of the postdoctoral work. The Center for Digital Narrative’s Computational Narrative Systems node, led by Nick Montfort, studies and researches the computational modeling of narrative, which includes developing new