PhD Position in Agent-Based Modelling of Urban Economic Segregation

Economic resources (e.g. wealth and income) are distributed unevenly among people and in space: within cities and between cities, people and resources are concentrated and segregated. Economic inequality and urban segregation are considered top priority challenges by the United Nations. Tackling them is urgent because economic inequality and urban segregation restrict the ability of the poorest individuals to get by in life, but they also affect life expectancy, social justice and cohesion for everyone.

One way to understand and address urban economic segregation is to model its dynamics, and to run and compare policy scenarios on how to reduce it. Following recent developments in analytical sociology and computational social sciences, generative modelling (such as Agent-based modelling) has proven valuable to implement, combine and test causal theories through mechanistic representation. By showing how urban economic segregation persists and evolves due to individual interactions in space, we aim to advance our understanding of the interplay between economic inequality and residential segregation and to better assess possible remediatory policies.

This doctoral position represents an exciting opportunity to combine and develop the latest methods of modular social simulation, empirically-calibrated agent-based models and spatially explicit population synthesis on a socially relevant topic. You will be expected to 1/Build a synthetic population from sociodemographic and spatial microdata ; 2/ Build a modular Agent-based model of urban economic segregation, that translates a set of identified theories into social mechanisms ; 3/Evaluate and calibrate the model configurations against sociodemographic and spatial microdata ; 4/ Implement, run and compare policy scenarios on the calibrated model ; 5/ Write, submit and present related articles to relevant journals and conferences.

This research programme should lead to two areas of innovation. First, you will advance research on urban economic segregation by providing a theoretically grounded and empirically calibrated agent based model on which to test policy scenarios. Second, you will participate in on-going methodological research on spatially explicit synthetic populations and modular model building (including reusable building blocks) in the field of social simulation. Please apply if you think these challenges are exciting opportunities for you to grow as a computational social scientist.

In order to meet the challenges of this doctoral project, you should have experience in the fields of spatially-explicit social simulation, model evaluation and population synthesis. social science and spatial sciences. The successful candidate will be funded by the ERC Starting Grant SEGUE and will work in close collaboration with its members: a PhD student, a postdoctoral researcher and the Principal Investigator Dr. Clémentine Cottineau. You will find support within the Urban Studies section of the Urbanism Department of the faculty of Architecture and the Built Environment – a recognised centre of expertise in working on urban inequality with longitudinal microdata –, and link up with the Multi-actor systems group at the TPM faculty of TU Delft.

Specifications

36—40 hours per week €2770—€3539 per month Delft

Delft University of Technology (TU Delft)

Requirements

Need to have:

Completed Masters degree in computational social science, analytical sociology, complex systems science, urban studies, spatial economics, social statistics, economic geography or other relevant fields linked to agent-based modelling and spatially orientated quantitative social science; Knowledge and experience in agent-based modelling (model building, analysis, evaluation); Knowledge and experience with population synthesis; Knowledge and experience with object oriented programming; Interest in multidisciplinarity research; Ability to handle and combine the abstract concepts and theories of inequality and segregation; Ability to work in a team, take initiative, be creative; Good command of verbal and written English.

Nice to have:

Experience of linking agent based models with large empirical data; Interest in open source software and scientific reproducibility. 

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the .

Conditions of employment

Fixed-term contract: 4 years.

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2770 per month in the first year to € 3539 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a for partners and they organise events to expand your (social) network.

Employer

Delft University of Technology

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core and we actively to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact

Department

Faculty Architecture & the Built Environment

The Faculty of Architecture and the Built Environment has a leading role in education and research worldwide. The driving force behind the faculty's success is its robust research profile combined with the energy and creativity of its student body and academic community. It is buzzing with energy from early in the morning until late at night, with four thousand people studying, working, designing, conducting research and acquiring and disseminating knowledge. Our faculty has a strong focus on 'design-oriented research', which has given it a top position in world rankings.

Staff and students are working to improve the built environment with the help of a broad set of disciplines, including architectural design, urban planning, building technology, social sciences, process management, and geo-information science. The faculty works closely with other faculties, universities, private parties, and the public sector, and has an extensive network in the Netherlands as well as internationally.

Click to go to the website of the Faculty of Architecture and the Built.

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