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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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AI / ML. The unique inter-disciplinary combination will enable: (i) a-priori biological knowledge infusion for GRN modeling and developing GenAI methods for generating GRNs; (ii) generating simulated
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robots, tractor-implement automation, communication technologies for vehicles, navigation, guidance and planning, positioning systems, model-based control of mechatronic systems, drives and power systems
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simulations to model this process and, in conjunction with ongoing experimental studies, obtain design rules for the optimum crown ether, lithium counter-ion, and solvent, which will lead to enhancements in
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cross-disciplinary research areas and/or application areas such as the ones listed below: CROSS-DISCIPLINARY RESEARCH Nanomaterials &Nanofabrication Nanocharacterisation Modelling and simulation
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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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to study corrosion, cracking and mechanical degradation, develop advanced computational models using modern C++ and high-performance computing to simulate material behaviour over a 100+ year timespan. This
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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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. These models will then be adapted and applied to other ComDisp case studies in the USA, Ecuador, and Turkey. The PhD candidate will be responsible for: • Developing, testing, and analyzing hybrid simulation