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) transport; • Are familiar with chemical simulation techniques, including but not limited to density functional theory, molecular dynamics, (kinetic) Monte Carlo modeling, finite-element modeling, and multi
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the application of machine learning and artificial intelligence. By using neural networks developed in Python, the project aims to generate robust and generalisable models for scaffold design. Industrial
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evaluating CRN ML models 3. Designing and developing CRN ML architectures for tasks of chemical interest, including reaction property prediction and reaction link prediction. 4. Predicting the properties and
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of nanoparticle-loaded scaffolds; quantify bone healing, osteointegration, and defect closure in critical-size bone models, and establish dose–response relationships for effective scaffold-mediated delivery
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on understanding and improving the cell systems used to create these therapies with a particular emphasis on the cellular molecular mechanisms involved. We apply advanced omics technologies to identify the genes and
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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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group. Advancements in tissue model development are central to the goals of Print4Life, which seeks to revolutionise how functional, bioengineered structures are designed and fabricated. Within
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project will investigate the behaviour and design of rock-socketed anchors through a combination of laboratory experiments and advanced numerical modelling. Laboratory experiments will explore anchor