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Field
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to shape how humans and AI interact in high-stakes contexts. You will help define how AI can support—not replace—human judgment, ensuring that technology empowers rather than undermines trust and autonomy
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at the tumour margin represent a key target for earlier and more effective therapeutic intervention. This PhD project will develop advanced MRI analysis methods and imaging-driven predictive models focusing
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biological mechanisms is necessary to establish mechanistic models for tree distribution and growth that will improve our predictions of tree species' range-shifts as well as productivity changes within
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scale AI-driven technologies in ways that make a true difference to society. Our ability to respond to the opportunities afforded to society will depend on training and building a workforce that is AI
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vegetation-enhanced performance will have significant application. Working in the University’s world class COAST Laboratory, you will develop and validate physical modelling techniques to represent vegetation
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mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional
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mangament in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction. Scientific context Many engineering and
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In this project, the selected candidate will join us in conducting research in statistical learning, developing data-driven methods to learn models of large-scale signals and systems from data
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cartilage tissue engineering? Are you driven to develop novel in silico frameworks that deepen mechanistic understanding of tissue growth and inform in vitro experiments? Then you might be our next PhD
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systems. By combining microclimate modelling, remote sensing data, and data-driven methods, the results are integrated into a Digital Twin framework. The research will support predictive risk assessment and