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investigates the thermodynamic modelling of chloride migration in concrete exposed to accelerated chloride ingress conditions, with particular focus on replicating and predicting the results of the NT Build 492
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will undertake doctoral research focused on activity-based travel behaviour, household energy use, and sustainability policy, contributing to the development of causal inference and agent-based modelling
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socio-marine ecosystem models Marine and coastal socio-ecosystems such as Dublin Bay are highly complex systems subject to accelerated climate change. They are characterised by interacting biological
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developing representations, using neuroimaging with fMRI and OPM-MEG, and to model the developmental process using computational models from AI. The project aims to better understand infant cognition, develop
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on the atomistic and first-principles modelling of spin and magnetism in low-dimensional systems, including stacked 2D materials and 2D metal–organic frameworks (2D-MOFs). Research will explore emergent magnetic
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enables the fabrication of spatially complex metal structures with features on the micron-scale. However, our capacity to intelligently design these surfaces is 2 limited, because the models that predict
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PhD studentship on developing a living lab digital twin to integrate catchment models and climate adaptation measures for catchment management in Ireland. This PhD is part of a larger interdisciplinary
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competitiveness assessment. The main tasks will be to develop models to assess life cycle cost, environmental and social impacts of mushroom production to evaluate mushroom sector sustainability. Supervision
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models of mushroom farming. This project aims to support the transition towards technology adoption in the mushroom sector through an environmental, social and competitiveness assessment. The main tasks
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finite element modelling, experimental characterisation, and data-driven optimisation, the project aims to establish quantitative relationships between structural parameters and mechanical performance