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. Quantitative, computational, or mixed-method approaches are particularly encouraged, including but not limited to geospatial analysis, machine learning, predictive modelling, and causal inference techniques
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. Of particular interests are (directed) hypergraph neural networks, which can be used to predict vertex (molecule), hyperedge (reaction), or hypergraph (network) features. While this project will be heavily
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of the bioprinting process. Objective 2: Training of a deep learning model to predict inputs that will achieve bioprinted scaffolds with the required print fidelity and scaffold micro-architecture. Objective 3
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learning-based atomistic simulations to predict (electro)chemical reaction pathways 2. Designing and implementing multi-scale electrochemical simulations combining elementary reaction mechanisms with multi
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response under representative loading conditions, while simulations will provide a framework for predicting long-term performance. By integrating experimental and numerical approaches, the research will
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flow behaviour, droplet formation, freezing dynamics, and ice crystallisation effects, integrating experimental data to refine predictive models for process performance and construct stability. Task 3