Higher-order coordination and emergent communication across human, AI, and animal systems Northeastern University London in United Kingdom

Northeastern University London offers a unique PhD opportunity in the interdisciplinary field of network science, focusing on higher-order coordination and emergent communication across human, AI, and animal systems. As both a UK university and the European campus of Northeastern University (Boston), NU London provides students with access to international research networks and the chance to engage with overseas campuses during their doctoral studies.

This project, part of the ERC Consolidator Grant “RUNES” (Reconstruction and Unification of Neural and Ecological Systems), investigates how group interaction structures shape coordination, norm formation, and the emergence of communication systems. The research spans three cognitive system classes: human populations, multi-agent AI systems, and animal societies. The approach integrates online behavioural experiments, multiagent simulations, and analysis of animal communication data.

Key research areas include:

• Social coordination on group interaction structures: Extending the naming-game framework to higher-order topologies, the project designs and runs large-scale online coordination experiments, varying group size, overlap, and clustering. The aim is to test how group-level interaction structure shifts tipping-point dynamics and convention formation, using higher-order contagion models and information-theoretic decompositions.
• Emergent communication in multi-agent AI systems: The project explores how interaction topology affects the complexity and compositionality of communication protocols in multiagent systems. It leverages recent findings on LLM agent populations and uses reinforcement-learning agents for mechanistic validation, measuring communication complexity with information-theoretic tools.
• Animal communication and collective coordination: Using data from Project CETI (sperm whale vocalisations and behaviour) and collaborating labs (zebrafish collective behaviour with neural activity markers), the project applies higher-order statistical analysis to predict collective outcomes beyond pairwise models.

The project culminates in a cross-system synthesis, examining whether topological and information-theoretic features predicting coordination in one system transfer to another—a foundational question for complex systems science.

The successful candidate will join the NP Lab within the Network Science Institute, led by Professor Giovanni Petri, and interact with a vibrant, interdisciplinary research environment. The supervisory team includes Professor Giovanni Petri (NU London) and Dr. Zhongtian Sun (University of Kent). The PhD is aligned with the Network Science programme and is full-time.

Funding: The position is fully funded, covering tuition fees, an annual stipend, and a London allowance (UKRI rates) for 3.5 years.

Eligibility: Applicants must have a Bachelor’s degree in a relevant subject (2:1 or 1st class, essential) and preferably a Master’s degree in Physics, Mathematics, Computer Science, or a related quantitative field. Strong modelling, computational, and code development skills are required, with experience in network science, multi-agent systems, statistical mechanics, or computational social science. Good programming skills in at least one of Python, Julia, Rust, or C++ are essential. IELTS 7 overall (with at least 6.5 in each component) or equivalent is required for non-native English speakers. Applications are open to UK and international students, but visa costs cannot be supported.

Application deadline: April 30, 2026.

How to apply: Send a CV, a 2-page research proposal related to the project topics, and a covering letter stating how you meet the requirements and your interest in the research. Apply via the provided link and reference your application ‘R139425’. Informal enquiries can be directed to Professor Giovanni Petri ([email protected]).

This PhD offers a stimulating environment for candidates excited by interdisciplinary research at the intersection of network science, collective dynamics, and communication systems.