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group, we synthesise these functional nanomaterials from the bottom-up, using protocols of molecular beam epitaxy and on-surface supramolecular chemistry. We study these systems by means
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acute pathology. The development of such a molecular imaging probe for direct and sensitive detection of fibrosis during the early stages of pathology would represent a true breakthrough in the field
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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known as Team COMPAS -- includes a number of amazing undergraduate and graduate students, postdocs, alumni, and other fantastic collaborators. Please contact me if you are interested in joining our group
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theoretical colleagues. All research takes place within our dynamic particle physics research group with academics and postdocs, as well as graduate and undergraduate students. Some work will be purely
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networks. C2 research that better reflects the rich dynamics and complexities that occur in real-world contexts has the potential to result in major benefits for many sociotechnical systems. Therefore
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systems, tephrochronology, and Quaternary geochronology. These tools will be applied, for example, to study the dynamics of active caldera complexes (e.g., Toba in Indonesia and/or Cerro Blanco in Argentina
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dynamics model that incorporates a range of net zero technologies and lifestyle solutions along with social and political drivers and barriers to adoption. By doing so, it will guide and enable policymakers
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of Excellence, the successful applicant will also be part of dynamic, national network of collaborating universities and industry partners, offering ample opportunities for national / international collaborations
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will generate new knowledge in the area of IoT Trust by developing novel techniques to establish trust in highly dynamic crowdsourcing IoT environments. The project's main outcomes include