Research Assistant

Location: This role is based at One Portsoken, Portsoken Street, London E1 8PH (Hybrid role). Hybrid working is available by arrangement with the Principal Investigator, with a minimum of three days per week on-site to support close collaboration with the wider project team.

Term: Full-time for 3 years starting 4^th of January 2027

Benefits: The university supports staff maintaining a good work/life balance, offering flexible working and parental leave opportunities, an Employee Assistance Programme as well as optional private medical insurance, season ticket loans and a cycle to work scheme. Tuition fee remission is also available.

Employees are automatically enrolled in the University’s pension scheme with a minimum 4% contribution. They may also join the Salary Sacrifice plan to make additional tax efficient pension contributions. The University matches 4% as standard, rising to 8% maximum for higher contributions. Employees can tailor their contributions with support from an appointed independent financial advisor. 

Direct Reports: None – this is an individual contributor role with no line-management responsibilities.

Reports to: Prof István Z. Kiss (Principal Investigator) and Dr Andreia Sofia Teixeira (Co-Investigator)

Start: 4^th of January 2027 

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. 

The COVID-19 pandemic exposed the limits of epidemic models that treat human behaviour as an afterthought. This project, "Rethinking Epidemic Models: Integrating Human Behaviour and Psychology", moves beyond simplistic rational-choice or imitation-based assumptions to build models grounded in how people make decisions, through their social identities, group norms, and personal risk judgements. The Leverhulme Trust, one of the UK's most prestigious funders of "original and adventurous research", has backed this genuinely interdisciplinary approach, which challenges the boundaries of mathematical epidemiology and social psychology alike. 

You will be based at the Network Science Institute part of Northeastern University London, a vibrant, growing centre for network science, sitting within a wider Institute that also has a presence in Boston and Portland, Maine, with regular exchange of ideas. 

Why join us: 

• Work at the leading edge of a genuinely new field, designing and building an agent-based modelling (ABM) platform that couples disease transmission with psychologically-grounded behavioural rules – work that will directly shape how future pandemics are modelled and managed.
• Join a truly international, multi-institutional, interdisciplinary team: Professor István Kiss and Dr Andreia Sofia Teixeira at the Network Science Institute, NU London, Professor John Drury at the University of Sussex (a participant in the UK Government's SPI-B group during COVID-19), and Dr Marijn Stok at Utrecht University/RIVM (formerly of the Dutch Corona Behaviour Unit).
• Help build genuinely open science: you will develop and prepare for public release an open-source ABM platform, with your code and methods used and cited well beyond the life of the project.
• Publish in leading interdisciplinary journals and present your work both to network science and psychology audiences (e.g. NetSci, the European Health Psychology Society) and to the multi-agent systems and AI community (e.g. AAMAS, IJCAI) — since a well-built ABM platform has real value and visibility in the computer science world too.
• Build connections across network science, epidemiology, and psychology through regular exchanges with colleagues at Sussex and Utrecht. 

Duties and 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. 

Person specification criteria 

To undertake this role, the following should apply – should you not have the experience below, please do highlight where transferrable skills would assist with you undertaking the role. 

• Experience
• Knowledge, Skills and Abilities
• Education, Qualifications and Training
• Personal Attributes 

Experience 

• 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. 

Knowledge, Skills and Abilities 

• 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. 

Education, Qualifications and Training 

• A 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. 

Personal Attributes 

• 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. 

Additional Information 

Enquiries 

Informal enquiries may be made to Prof István Zoltán Kiss (Istvan.kiss@nulondon.ac.uk) or Dr Andreia Sofia Teixeira (sofia.teixeira@nulondon.ac.uk).  However, all applications must be made in accordance with the application process specified. 

Application process 

Please reference your application “R141807”. 

Applications should be made via the 'Apply' button above by the 21^st of September 2026. 

Applications must include a covering letter and a full curriculum vitae. 

The panel will be shortlisting for this position on a rolling basis so please apply as soon as possible. We reserve the right to close this post before the closing date if we receive a large number of applications. 

Interviews are expected to commence w/c 28^th of September 2026. 

Participation in the equal opportunities section is encouraged, but voluntary. 

Please note this role may require a basic or enhanced DBS check.  Our organisation acknowledges the duty of care to safeguard, protect and promote the welfare of our students and staff, and is committed to ensuring safeguarding practice reflects statutory responsibilities, government guidance and complies with best practice and Ofsted requirements. You must adhere to the above if you are offered a role with NU London. 

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. Candidates must be able to demonstrate their eligibility to work in the UK in accordance with the Immigration, Asylum and Nationality Act 2006. 

Job sponsorship: The University may be able to provide skilled worker visa sponsorship for this position, depending on individual circumstances.