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(e.g., taxonomy, evolution, ecology, and geology). You will receive dedicated supervision from an experienced researcher and work closely with several international experts and collaborators with whom
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and economic use of renewable electricity without an unnecessary discarding of generated power. This four year’s PhD position is aimed at developing multiphase models to predict the dynamics and
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in high-tech, medical, and energy systems. Information Modern engineering increasingly relies on data-driven models to describe complex dynamical systems. Scientific machine learning is now enabling a
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-year research and innovation programme developing next-generation circular steels that can safely operate in hydrogen environments. By combining advanced experiments with multiscale modelling, CIRHY aims
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actors involved. Positioned at the intersection of behavioural science, agricultural economics, and agricultural systems modelling, this PhD project will contribute to the development of modelling tools
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AI, decision support, and data-driven discovery. Our group develops computational methods and theory for generating, analyzing, and interacting with visual data. Our research spans computer graphics
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the project "Circularity as Opportunity: Engineering Hydrogen-Resistant Circular Steels (CIRHY)", a 6-year research and innovation programme developing next-generation circular steels that can safely operate in
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, you will: Acquire and analyse human neuroimaging data, with a primary focus on high-field fMRI of natural sound perception. Develop and apply AI/NeuroAI models, including deep neural networks, to model
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spatial scales into a crop modelling framework and predicting daily targets for photosynthesis and transpiration rates (all developed by other researchers in the project consortium). Based on these targets
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of (standardised) ontologies. Central to the framework is the Architectural Ontology Recommendation Service, which leverages Enterprise Architecture models (e.g., ArchiMate and EIRA) to derive semantic requirements