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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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completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted. Other information for consideration, e.g., BSc/MSc thesis
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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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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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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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experience in the following fields. Cyber-physical modelling and simulation Digital Twins Autonomous Agents and Multi-Agent Systems Machine Learning and MLOps Probability & Statistics incl. Python/R Place of
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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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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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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