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combination with multi-fidelity response models. The multi-fidelity models may include combinations of physics-based response models, Artificial Intelligence (AI) models and probabilistic methods. Your
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amine-based CO2 capture plants. This position at the Department of Chemical Engineering at NTNU focuses on chemical absorption and, more specifically, on the modelling of solvent degradation. Carbon
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metocean and biogeochemistry models accounting for climate change impacts across Europe (from the Nordic Seas to the Black Sea and Mediterranean Sea) and the Pan-Arctic region. The PhD project focuses on new
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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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The fellowship period is three years. The PhD candidate will work on the project entitled "Male Reproductive Disorders of Foetal origin – Development of an Organoid Model for Mechanistic Insight". Foetal life is a
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systems such as fuel cells and batteries. Intelligent control and management systems will be designed to enhance efficiency, sustainability, and seamless integration. Developed models and strategies will be
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Additional preferred expertise and experience Experience with longitudinal data analysis and advanced statistical methods (e.g. linear and generalized mixed-effects models, growth curve analysis and structural
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of Biostatistics, Institute of Basic Medical Sciences (IMB), University of Oslo (UiO), Norway. The candidate shall take part in the research group on “Statistical models for high-dimensional and functional data
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project “ComDisp: Community-Centered Modeling of Housing-Related Health Disparities.” ComDisp develops a grassroots modeling framework to predict health disparities under different climate change scenarios
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/work tasks: The position is part of the Belmont Forum project “ComDisp: Community-Centered Modeling of Housing-Related Health Disparities.” ComDisp develops a grassroots modeling framework to predict