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Job Synopsis Reporting to the Head of School, the post holder will play a key leadership role in the School of Medicine, leading and managing the distributed administrative team on UL campus and
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the distribution of expression levels in tens of thousands of single cells. In this project, we will utilise state of the art single cell omics to decipher the biological processes underpinning therapeutic rAAV
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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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science and engineering fundamentals of building engineering. Proficiency in electrical systems design for buildings, domestic and commercial. Expertise in the design of electrical distributions systems
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distribution of resources, of infrastructural development and usage optimisation in a shared facility. Applicants must have essential qualifications and be able to demonstrate relevant experience in animal
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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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development; knowledge of operating large multi-strand budgets, of the effective allocation and distribution of resources, of infrastructural development and usage optimisation in a shared facility. Applicants
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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
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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