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Japan will be analysed in tandem with a complementary genomic transect from Ireland, enabling comparative insights into how evolutionary history at the edges of Eurasia has shaped differences in disease
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networks, making use of intelligent AI-driven control planes. The work will involve: i) (i) development of network probing algorithms for OSNR/GSNR measurement automation; (ii) development of adaptive ML and
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the utility and the robustness of different explanation strategies. A large focus of this project will be on leveraging novel and interpretable approaches in applied domains such as algorithmic fairness and
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scientists from, e.g., Sociology, Political Science, or Economics. The PhD candidate will also develop state-of-the-art optimisation algorithms specifically tailored to the proposed large-scale network models
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the latest advances in AI to identify factors in pregnancy, labour and in the early neonatal period that represent increased risks for CP. We will develop novel AI algorithms for the analysis of physiological
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evaluation. Previous knowledge of belief/trust for vehicular cooperative perception is a distinct advantage as is experience with algorithm development and networking simulation. Candidates should have
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, computer science, or a related field is required, together with relevant expertise in areas related to Artificial Intelligence, such as: Foundational Models, Algorithmic Research Machine/Deep Learning Computer