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Field
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hydrogen environments. By combining advanced experiments with multiscale modelling, CIRHY aims to enable reliable and sustainable steels for future hydrogen infrastructure and industry. Hydrogen
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largely by the rapid expansion of artificial intelligence (AI), cloud computing, and high-performance data processing applications. As AI models continue to increase in size and computational complexity
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for identifying concentration, floe size, geometry, and possibly stage of development. The plan is to build models so that radar measurements alone can be used to populate, as far as possible, the Stage
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modelling for conversion and sorbent regeneration, in conjunction with another PhD student in the department who will perform CFD modelling and other researchers performing process system modelling within
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of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
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support adaptive and automated process execution.• Extending BPM models, notations, methods, and technologies to enable sustainability-driven decision-making.• Evaluating and validating proposed solutions
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to investigate the potential of using Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the
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, these flows remain poorly understood. As a result, even the most basic properties cannot be predicted reliably. For instance, the best available models over- or underestimate the measured pressure drop in a
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two clinical studies in Kenya and Zambia, assessing whether E. coli carriage contributes to Enterobacteriaceae colonization and ARG transmission. Finally, the PhD student will use agent-based modeling
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multi-fidelity modelling, your research will advance and integrate three core elements: (i) physics-based multi-fidelity structural models enabling high-resolution analysis at fatigue-prone hot-spots