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fields such as data science, computer science, and mathematics, that work together to develop innovative methods and tools to analyze large-scale data sets and gain insights into the underlying principles
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that LLM agent populations spontaneously develop shared social conventions through naming-game interactions, we will test whether convention formation dynamics on group topologies mirror those observed
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develop a unified framework for reliable counterfactual analysis in intelligent networks, combining tools from causal inference, agentic AI, and statistical learning. The successful candidates will: Have a
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their London-based doctoral studies, providing a truly unique and highly sought-after dimension to their research training. The Project The project aims to develop reliable, adaptive, and universal statistical
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to develop scalable and energy-efficient AI accelerators for resource-constrained IoT and networked systems through joint optimization of AI models and emerging hardware architectures. Topics will include: AI