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About the Role

Help build the next generation of epidemic-behaviour models. This is a rare opportunity to join a pioneering, three-year Leverhulme Trust-funded project that reimagines how epidemic models capture real human behaviour, bringing together mathematical epidemiology, agent-based modelling, and contemporary social and health psychology to fundamentally rethink how we predict and respond to disease outbreaks. You will be based at the Network Science Institute part of Northeastern University London, a vibrant, growing centre for network science.

Responsibilities

Design and develop a comprehensive agent-based modelling (ABM) platform that integrates disease transmission dynamics with psychologically-grounded behavioural rules, incorporating multi-layered decision-making (group membership, information trust, and risk–vulnerability trade-offs).
Conduct model parametrisation and systematic sensitivity analyses to identify the behavioural-epidemic feedback mechanisms with the greatest impact on epidemic severity.
Estimate unobservable behavioural parameters using Approximate Bayesian Computation (ABC) and implement time-series cross-validation to test the models’ predictive performance.
Perform comparative analyses against traditional (behaviour-free) epidemic models, using measures such as AIC, BIC, and prediction accuracy, to quantify the performance gains from incorporating psychological realism.
Prepare the ABM platform’s code and documentation for open-source public release, ensuring it is modular and adaptable across different disease and behaviour scenarios.
Work closely with the project’s PhD student, based at the University of Sussex, to integrate psychologically-grounded behavioural modules into the ABM framework as well as with Professor John Drury at the University of Sussex, and Dr Marijn Stok at Utrecht University.
Contribute to the extraction and integration of behavioural and epidemiological data from international datasets (UK, Netherlands, USA) and historical outbreaks (2009 H1N1, 2003 SARS).
Lead and co-author peer-reviewed publications and present findings at interdisciplinary conferences and contribute to the project website and to non-specialist dissemination materials.
Participate in regular project meetings and research visits across NU London, the University of Sussex, and Utrecht University to ensure effective cross-institutional collaboration.

Requirements

Proven experience in agent-based modelling (ABM) and/or computational or mathematical modelling of infectious disease spread.
Experience translating theoretical or conceptual constructs into computational rules or algorithms.
Experience with statistical/computational inference methods such as Approximate Bayesian Computation, Monte Carlo simulation, or model comparison techniques (e.g. AIC/BIC).
Experience working in an interdisciplinary research environment is desirable.
Strong programming skills (e.g. Python, R, or C++) and experience developing and documenting research software.
Sound understanding of mathematical epidemiology and network science (e.g. compartmental models, epidemics on networks).
Ability to critically engage with concepts from social and health psychology (e.g. social identity, risk perception, health behaviour theories) and translate them into modelling rules.
Strong analytical and problem-solving skills, with the ability to validate models against real-world data.
Excellent written and verbal communication skills, including the ability to present technical work to non-specialist audiences.
Highly self-motivated and able to work independently as well as collaboratively within a multi-institutional team.
Good organisational and time-management skills, with the ability to manage multiple concurrent work packages.
Willingness to travel periodically to project meetings and conferences (NU London, University of Sussex, Utrecht University).
A collaborative, open-science mindset, with commitment to producing well-documented, reproducible research code.
Applications must include a covering letter and a full curriculum vitae.
Candidates must be able to demonstrate their eligibility to work in the UK in accordance with the Immigration, Asylum and Nationality Act 2006.

Skills

Agent-based modelling (ABM)
Computational modelling
Mathematical modelling
Infectious disease spread modelling
Statistical inference
Computational inference
Approximate Bayesian Computation (ABC)
Monte Carlo simulation
Model comparison techniques (AIC/BIC)
Programming (Python, R, C++)
Research software development
Mathematical epidemiology
Network science
Social psychology
Health psychology
Analytical skills
Problem-solving skills
Model validation
Written communication
Verbal communication
Organisational skills
Time-management skills

Location

One Portsoken, Portsoken Street, London E1 8PH

Work Type

Hybrid
Full-time

Experience Level

Depending on experience

Education Level

PhD (or near completion) in a relevant quantitative discipline such as applied mathematics, physics, computer science, network science, computational epidemiology, or a closely related field.

Salary/Compensations

£43,555 - £46,195 per annum

Benefits

Flexible working
Parental leave opportunities
Employee Assistance Programme
Optional private medical insurance
Season ticket loans
Cycle to work scheme
Tuition fee remission
University pension scheme with minimum 4% contribution (University matches 4% standard, rising to 8% maximum)
Salary Sacrifice plan for additional tax-efficient pension contributions
Support from an appointed independent financial advisor
25 days annual leave, plus 8 bank holidays and winter break holidays
Access to personalised Continuous Professional Development (CPD) plans and opportunities
Eye test reimbursement
Access to deals and discounts for food & shopping in the local area

About the Company

Northeastern University London (NU London) is a prestigious higher education institution based in the heart of London and is part of Northeastern University’s global campus network.
Overlooking the River Thames near Tower Bridge, NU London offers academically challenging educational programmes designed to inspire innovative thinking, encourage interdisciplinary study, and provide global experiences.
The bright and modern campus offers award winning, contemporary facilities for students and staff including state of the art audio visual technology in its teaching and meeting spaces.
Inspired by excellence, infused with an energy of ideas and ability in motion, at NU London, being a part of our staff is to be a part of a collective of entrepreneurs and educators, builders and thinkers.
NU London is growing quickly, offering opportunity and growth for our staff. Currently hosting 1,500 students, our aim is to have 4000 students by 2028/29.

Equal Opportunity

Applications are welcome from all sections of the community and will be judged on merit alone.
We welcome applications from all underrepresented groups, including the Global Majority.