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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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psychiatry on longitudinal multimodal data, to fit and validate prediction models. Perform quality control and imputation of genotype data from relevant datasets, including international and Norwegian samples
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
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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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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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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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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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, 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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the ability of the mono-ADP-ribosyltransferase, Parp7 and Parp11, at improving check point inhibition therapy in intraductal mammary tumour models. More about the position Research activities will take place in
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infrastructure, models for interdisciplinary collaboration and training in research, including computational and transferable skills. The DSTrain program also includes a range of cross-sectorial secondments