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will conduct sampling, and apply experimental methods such as metagenomics and metatranscriptomics, linked to soil and emission data to help create predictive models. Within a broader framework, your
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failure analysis using advanced finite element models and simulation techniques. This is enabled by digital and sensor technologies such as artificial intelligence, computer vision, drones, and robotics
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results with AI models and system simulations to create a digital twin of the PtX process for predictive optimization and scenario analysis. Funding This PhD position is generously funded through the Villum
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delivering influenza vaccination through intramuscular and intranasal routes, which will be compared to a live influenza human challenge infection model in humans. Methodology will involve implementation
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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to the PhD project ie. processing and analysis of dietary intake data, statistical analyses (eg. linear mixed models) as well as evaluation of child growth and body composition data. Relevant publications
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linked data Sensors as part of Internet of Things (IoT) and integration of sensory information in simulation models as part of Digital Building Twins (DBT) during run-time Life cycle and sustainability
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group at CIE. The aim of the position is to build strong knowledge and competencies within the field of electrochemical storage device design, simulation and testing. Job description You will conduct
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equipment e.g. STM. Simulating fabrication methods. Collaboration with other groups at NQCP and companies/academic groups in and around the Copenhagen area. Join us in this major confluence of exciting
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of computational chemistry. Applicants can have a background from cheminformatics including RDKit, machine learning applied to chemistry, and molecular modeling Our group and research- and what do we offer? Our