伦敦东北大学招收信息系统博士
About the Project
As part of a major investment, Northeastern University London (NU London) has multiple, fully-funded PhD studentships available to accelerate its interdisciplinary research in the humanities, social sciences, and computing, maths, engineering and natural sciences. Each scholarship is fully-funded for three and a half years (UKRI rates) and includes full course fees, an annual stipend (including an additional London allowance) and associated costs, such as training.
NU London is both a UK university governed by UK higher education regulations, and the European campus of Northeastern University – a large, top-tier research intensive, Bostonbased institution. Founded in 1898, Northeastern University is known for its high-impact research, aimed at solving problems across the globe. Interdisciplinarity, experiential learning, and connection to partners beyond academia are at the heart of the Northeastern University ethos. Northeastern received $230.7m of external research funding in 2022, and is the recognized leader in experience-driven lifelong learning. It has campuses across the United States and Canada (in Boston; Charlotte, North Carolina; Portland, Maine; Oakland, California; San Francisco; Seattle; Silicon Valley; Arlington, Virginia; the Massachusetts communities of Burlington and Nahant; Toronto and Vancouver).
The Project
How do groups influence social and coordination dynamics in human and animal populations? What are the emergent patterns that arise from collective behaviour within groups? How can information theoretic descriptions help in detecting emergence in complex systems and understanding group dynamics? What is the role of causal decoupling in shaping the behaviour and coordination of individuals within groups, and in turn, how does downward causation occur within groups, and how does it impact individual decision-making and behaviour?
This research project aims to delve into the origin and effect of groups on social and coordination dynamics, employing an interdisciplinary approach that integrates data from genetic samples and animal behaviour observations, face-to-face interactions, digital social traces, and animal communication records. Additionally, we will explore information theoretic descriptions of group behaviour to uncover emergent patterns, causal decoupling, and the concept of downward causation.
Genetic underpinning of social contagion and coordination: Using data from the collaborating labs, we will investigate the genetic toolbox required for a number of social behaviours to emerge in diverse populations of cichlid fishes, both in the lab and in the wild. Examples of such behaviours include social contagion, buffering, coordination and copying. By integrating genetic data into the analysis, we aim to unravel the genetic factors that contribute to social contagion phenomena, shedding light on the interplay between genetic factors and social interactions.
Social dynamics, group behaviour and communication: Humans and animals often interact in groups, wherein individuals' behaviours are shaped and influenced by the collective actions and characteristics of the group. In particular, we will ask how the explicit inclusion of groups changes the emerging behavioural and dynamical properties of interacting systems. To do this, we will investigate theoretical models of opinion dynamics and social coordination, with and without internal biases to characterize the phenomenological landscape of possible states, when considering only pairwise interactions as opposed to when explicit group interactions are present.
Detecting and modeling emergence, causal decoupling and downward causation: Using multivariate information theoretic approaches, we will quantify the transfer of information within and between individuals, revealing patterns of emergence and the causal relationships that contribute to group dynamics. In particular, we will investigate both causal decoupling and downward causation in real world data to ascertain how the collective behaviour of a group emerges and become independent of the individual agents (causal decoupling) and how it can exert influence on the individual members, shaping their actions and decisions (downward causation), with the final aim to understand how groups shape the cognitive, behavioural, and decision-making processes of individuals within the group, and to recognize the interplay between macroscopic and microscopic scales.
The successful candidates will have :
• MSc (or equivalent) in Theoretical Physics, Mathematics, or Computer Science, or related topic.
• Strong modelling, computational, and code development skills.
• Previous experience with network science and/or collective information processing is a plus.
• Experience in complex networks, statistical mechanics or topological data analysis.
• Have good programming skills in at least one of the following languages: Julia, Rust, Python, C++, Matlab.
• Fluency in English, both spoken and written.
• Highly collaborative spirit and personal initiative.
• Excellent personal skills
• An inquiring mind and the desire to challenge themselves
The successful candidates will benefit from a brand new campus on the banks of the River Thames next to Tower Bridge. This is an interdisciplinary, vibrant research environment with international collaboration and networking opportunities and dedicated research space. It will form the hub of a highly experienced, multi-institution supervisory team from NU London, Northeastern University and the University of Kent. In addition, successful candidates will benefit from the unique connection to the wider Northeastern University network in North America, providing a range of additional research opportunities and learning resources.
Shortlisted candidates will be interviewed in November. Candidates are welcome to contact the NU London supervisor with informal enquiries before the application deadline: giovanni.petri@nulondon.ac.uk
Project start date: 29 January 2024
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