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, and how plausible memory models can predict diverse quantitative data in linguistics and cognitive science. The project has two components: an empirical one and a computational one. The empirical strand
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You will develop and validate advanced AI models that integrate medical imaging, multi-modal clinical and omics data, and explainable AI (XAI) for the prediction of hepatocellular carcinoma (HCC
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) multi-omics analysis for the identification of therapeutic targets (biomarkers or druggable targets). The candidate will also develop and implement AI-driven tools to predict disease prognosis and
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physics-based theory with data-driven models, you will contribute to the next generation of predictive tools for materials design and discovery. You will also collaborate closely with experimental and
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is to determine how memory mechanisms can be generalised across linguistic domains, from the lexicon through syntax to discourse semantics, and how plausible memory models can predict diverse
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to discourse semantics, and how plausible memory models can predict diverse quantitative data in linguistics and cognitive science. The project has two components: an empirical one and a computational one. The
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imaging, multi-modal clinical and omics data, and explainable AI (XAI) for the prediction of hepatocellular carcinoma (HCC). Your research will directly contribute to early detection and risk stratification
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subsurface hydrogen storage and calibrate reaction parameters using experimental data. Perform numerical simulations of subsurface hydrogen storage including chemical reactions for an ensemble of geological
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data. Perform numerical simulations of subsurface hydrogen storage including chemical reactions for an ensemble of geological models that were developed in the Rapid Reservoir Modelling software. Develop
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corrosion and embrittlement in reactor environments, remains incomplete, and key mechanical properties are scarce. Without reliable experimental data, our ability to develop predictive models