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Field
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quasi-experimental methods to identify causal effects and test the predictions of economic and sociological models. Examples of current research projects include: long-term impacts of neighborhoods and
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Norwegian Mechanistic Empirical pavement design system. The project will combine material characterisation, theoretical modelling and experimental validation to improve the prediction of pavement performance
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the prevention, prediction, and treatment of diabetes through artificial intelligence, computational biology, and precision medicine. The successful candidate will contribute to the development and application
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Strong background in control, robotics and machine learning, and experience in areas like model predictive control, adaptive control, reinforcement learning, robot perception or manipulation. Programming
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, and machine learning applications for developing predictive methods. Desirable requirements: The candidate must possess scientific maturity to solve problems, proficiency in English scientific
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CRISPRi/a screening (Cell Stem Cell 2024), 3) Deciphering pathogenic coding and non-coding variants linked to congenital heart disease in a cell type-specific manner (Cell 2022), and 4) Combining iPSC
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quasi-experimental methods to identify causal effects and test the predictions of economic and sociological models. Examples of current research projects include: long-term impacts of neighborhoods and
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, structured phenotypes, medical terminology, ICD codes, and available omics and health-data resources to detect patients with clinical patterns suggestive of hEDS/HSD. The position is inspired by recent
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predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon suppresses LCI, including its effects on grain boundary site competition and the
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antigens presented by MHC molecules. This curiosity-driven project develops cutting-edge AI technology for 3D TCR–pMHC modeling to improve neoantigen identification, TCR specificity prediction, and TCR