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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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risk. These studentships are ideal for candidates with a strong background in computational biology, bioinformatics, population genetics, and statistical genetics and an exceptional interest in
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for a PhD studentship to investigate the genetic underpinnings of neuropsychiatric disorders using cutting-edge long-read sequencing (LRS) technology. This project aims to generate whole-genome LRS data