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Field
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enabling on-demand, local, and potentially circular production of spare parts — but effective decision-making frameworks are still lacking. In this PhD project, you will develop models and methods
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sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and
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data, the consortium aims to develop a robust model that can better predict transplantation outcomes. This innovative approach will contribute to more reliable donor kidney selection, reduced rejection
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appendages using the (halo)archaea as a model. Studying the infection mechanisms of archaeal viruses can provide insight into the evolutionary history of viruses and help to understand adaptation to extreme
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learning models keep getting better, but how they make their decisions often remains unclear, because these depend on many incomprehensible model parameters that have been learned from data. For instance
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to distributed energy resources, enabling energy sharing, reducing grid congestion, and enhancing sustainability. The PhD project will focus on the governance models adopted for energy hub platforms. These include
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maintain robustness through evolution using live-cell imaging and multiscale modelling. Job description Cells are often described as intricate machines where proteins work together in a tightly coordinated
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a given discrete-event model that is safe with respect to certain requirements. Yet, the application of the approach is limited by the fact that tools for synthesis cannot always deal with the state
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phase equilibrium modelling facilitate the prediction of metal budgets of crustal magmatic systems (e.g., Weller et al., 2024 JPet, Soderman et al., 2025 Nat Geosci). Pseudosections, the bespoke phase
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analytical facilities hosted at UU’s Electron Microscopy Centre. Project description Recent advances in thermodynamic phase equilibrium modelling facilitate the prediction of metal budgets of crustal magmatic