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- NTNU - Norwegian University of Science and Technology
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Field
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of buildings by combining simulations with experimental data investigate human comfort based on the experience of the occupants of buildings building information modelling and digital twins development of user
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electron microscopy analysis, Raman spectroscopy, fluid inclusion analysis, potentially appropriate petrochronological methods, and 3D geological modelling. The project will be conducted in partnership with
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water
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computational skills (using R, modelling software, working on a remote linux-based server) and experience in analyzing Next Generation Sequencing data, including PCA, outlier analysis, GO-term enrichment analysis
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archaeological excavations and dating with climate modelling on the one hand and research on human minds and sociality on the other. The PhD position will be part of an interdisciplinary project with the goal
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energy-efficient buildings Methods and models for energy demand calculation of buildings and/or neighbourhoods Methods and models for environmental life cycle assessment of buildings and/or neighbourhoods
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prone to change and decline. One project component analyses and models the effects of climate-induced cryospheric changes on water flows. The other project component, to which you will contribute
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and develop frameworks and knowledge on planning and coordination of resources within and across projects. apply quantitative methodologies, such as simulation and analytical modelling, to develop
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leader will be the Head of Department. About the project Modern control systems rely on being at least partially predictive while digital twins also must maintain a state model of the targeted cyber
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other are developing regulations that provides both incentives and constraints for the energy transition and emission reduction. The research objective of the PhD is to develop models that captures