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structural bioinformatics and molecular modeling tools, as well as building on and further developing the datasets and codes from the PePrMInt project (Cf https://doi.org/10.1371/journal.pcbi.1010346
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selectivity of lipid transfer proteins. The project will exploit the wealth of available protein sequence and structure data through analysis using structural bioinformatics and molecular modeling tools, as
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areas within Energy, Climate and Environment and Regional Growth, and integrates the researchers from the Electrical Power Systems (EPS) research group (RG) and Applied Modeling and Control (AMOC) RG
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attachment. Main tasks Collect, compile, and analyze data to map the responses of plants and pollinators to climate change Participate in the development and adaptation of statistical models for analyzing
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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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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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part of a dynamic research environment focusing on the early mechanisms of neurodegeneration in Parkinson’s disease. The main tasks involve working with mouse models of Parkinson’s disease. Key
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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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status as “world-leading” as one of five appointed at the University of Oslo. More information can be found on https://www.lcbc.uio.no. Job description Develop quantitative models to estimate how the brain