Postdoc Synthetic Social Networks for Climate Attitude-Behaviour Diffusion

Do you want to build realistic social network models when the real networks cannot be shared Are you excited about developing privacy-safe synthetic networks that still preserve the dynamics that matter for diffusion and intervention analysis Join ROOTS at the University of Amsterdam and help deliver network generation methods and software that enable policy-relevant modelling of sustainable transitions without compromising data protection.

Join Us!

Help build the next generation of models for rapid climate action without the NWO Vidi-funded project ROOTS (Resilient Outcomes from Organic Transitions in Society) you will e.g. develop privacy-sensitive synthetic social network generators grounded in Dutch CBS microdata. Your work will enable policy-relevant modelling of how sustainable behaviours (e.g. adoption of solar PV and shifts in everyday choices) spread stall or reverse while respecting strict privacy constraints. You will also help develop mechanistic data-driven models to understand when sustainable behaviours (like adoption of sustainable technologies or daily behavioural choices) can spread rapidly through society and when interventions risk triggering backlash or polarization. You will join an interdisciplinary team at the University of Amsterdam working at the interface of complexity science behavioural science and policy.

This is what you will do

As a postdoctoral researcher you will lead the development and validation of synthetic but realistic social network models that can be used for diffusion and intervention simulations under privacy constraints. You will translate CBS microdata structures into reusable network-generation methods quantify how aggregation choices affect predicted tipping dynamics and deliver robust software and methodological guidance that can be reused beyond the Dutch context. There is flexibility for the research activities within the project beyond the synthetic network creation. You are expected to take an active role in departmental activities ranging from organizing seminars and group activities and stakeholder workshops to contributing to collaborative papers and grants.

Tasks and responsibilities:

The list of tasks is not exhaustive and covers some aspects of the project that may or may not be taken by the Postdoc. If there are sets of skills that you already have or would like to develop or a topic that you are particularly interested please mention those in your motivation.

• Derive behavioural models from individual-level data
• Develop mean-field agent-based and network-based models of collective behaviour adoption with heterogeneous preferences and reversible dynamics
• Link individual-level factors (e.g. costs norms infrastructure trust) to adoption outcomes in the domains of technology adoption (e.g. solar PV) and/or daily behavioural choices
• Develop privacy-sensitive synthetic network generation methods (multi-layer social networks demographic/spatial structure) compatible with CBS secure environments
• Define validation criteria for when is a synthetic network is fit for purpose for diffusion and intervention analysis
• Integrate network generators with agent-based / diffusion models developed within ROOTS
• Produce well-documented reusable software and reproducible pipelines (open where permitted)
• Contribute to the design and analysis of discrete-choice (conjoint) experiments and survey measures that parameterize and validate the models
• Contribute to partner-facing demonstrations and workshops (e.g. with CBS and other ROOTS partners)
• Support the development of behavioural models from individual-level data.
• Publish results in peer-reviewed journals
• Present at international conferences
• Participate in team activities at CSL and POLDER/IAS (workshops seminars) and contribute to collaborative papers
• Assist in teaching and supervision (e.g. tutorials MSc/BSc projects) in consultation with the group within the programmes MSc Computational Science and MSc Complex Systems and Policy
• Co-supervise projects aligned with the modelling/software work.

What we ask of you

• A completed PhD in network science computational social science statistics computer science physics applied mathematics or a closely related field
• You can translate messy real-world questions into clear model assumptions and testable mechanisms
• Demonstrable experience with quantitative analysis and programming (e.g. Python and/or R)
• You communicate clearly in spoken and written English and can write publishable academic texts
• You collaborate well in an interdisciplinary team and can work carefully under privacy/security constraints
• It is a preference if you additionally have experience with synthetic data multilayer networks secure data environments (e.g. CBS Remote Access) diffusion/agent-based modelling discrete-choice modelling complex contagion/threshold models computational economics causal discovery and/or simulation-based inference.

This is what we offer you

We offer a temporary employment contract for 30 38 hours per week for a period of 12 months with a probationary period of two months. Extending to 38 hours is possible if additional teaching responsibilities can be found. If we assess your performance positive we will extend your contract with 12 months to a total duration of 24 months. The preferred starting date is 1 September 2026 but it can be discussed.

The gross monthly salary based on 38 hours per week and dependent on relevant experience ranges between 3546 to 5538 (scale 10). This does not include 8% holiday allowance and 83% year-end allowance. The UFO profile Researcher 4 is applicable. A favourable tax agreement the 30% ruling may apply to non-Dutch applicants. The Collective Labour Agreement of Universities of the Netherlands is applicable.

Curious about our extensive secondary benefits package You can read more about it here.

You will work in this team

TheFaculty of Science has a student body of around 8000 as well as 1800 members of staff working in education research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works be it elementary particles the birth of the universe or the functioning of the brain.

You will work at the Computational Science Lab under the supervisions of dr. Vítor V. Vasconcelos embedded in the Informatics Institute (IvI) and POLDER at the Institute for Advanced Study (IAS) to connect complexity science to policy and societal impact. The ROOTS project is carried out in collaboration with Dutch societal partners including CBS (Statistics Netherlands) PBL Netherlands Environmental Assessment Agency TNO and the Ministry of General Affairs (DPC).

If you feel the profile fits you and you are interested in the job we look forward to receiving your application. You can apply online via the button. We accept applications until and including 30 April 2026. The first round of interviews will happen in the week of 12 May and you may receive an invitation with less than a week in advance; the second round of interviews will occur on the week of 18 May. These dates may be subject to delays. The project begins on 1 September. The intended start window is September 2026 to September 2027 (exact date to be agreed).

Applications should include the following information (all files besides your cv should be submitted in one single pdf file):

• a detailed CV including the months (not just years) when referring to your education and work experience;
• a letter of motivation;
• a list of publications and relevant projects;
• 12 representative papers or a writing sample (recommended);
• the names and email addresses of two references who can provide letters of recommendation.

A knowledge security check can be part of the selection procedure(for details:national knowledge security guidelines).

Only complete applications received within the response period via the link will be considered.

If you have any questions or do you require additional information Please contact:

• dr. Vítor V. Vasconcelos Associate Professor .