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-education-system/grading-system/?set_language=en ). Additionally, the candidate must include: a brief curriculum vitae (CV), a list of papers and publications (if any are available), and one copy of a
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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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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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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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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