Postdoctoral Researcher on the Impact of Climate Adaptation on Poverty Traps
Are you interested in interdisciplinary research projects? Do you like to work in a team of researchers working on societal challenges? We are seeking a post-doctoral researcher is interested in interdisciplinary research in the impact of climate change adaptation on poverty traps, and who likes to bridge research insights developed in computational science, environmental science, and developmental economics with practice.
As researcher you will be part of the Accelerating Scientific Discovery project ‘Computing societal dynamics of climate change adaptation in cities’. The project focuses on modelling the feedbacks between climate change adaptation and poverty traps. Specifically, we study the role of adaptation in shaping risk using spatially explicit modelling of the socio-economic dynamics related to climate change adaptation (CCA), and how dynamics in poverty levels impact adaptive capacity. Spatial agent-based modelling (SABM), enabled by growing computing power, has been successfully applied to analyse macro-outcomes in a virtual society, emerging out of the actions and interactions of individual heterogeneous agents in a city or region. Agent decision-making is flexible and incorporates behavioural factors, bounded rationality, interactions, and learning. Although SABM is actively used to study social, economic, and environmental problems, including CCA, they have not yet been used to explore the mutual dynamics of adverse climate impacts and poverty traps. You are expected to make methodological advancements in understanding the role of CCA in the emergence of poverty traps.
Further, to properly evaluate and understand the impact of a particular policy, it is necessary to develop natural-scale simulations and execute several simulation instances. Therefore, you should explore several strategies in scaling the SABM in collaboration with engineers from the Netherlands eScience Centre within this project. Speeding up massive SABM is crucial for their application to real world problems, especially when CCA decisions are to impact marginalized communities in cities on the Global South. You will specifically focus on urban informal settlements and collaborations with the Red Cross Red Crescent Climate Centre are foreseen.
You will be hired at the Computational Science Lab, Informatics Institute (IvI) in the Faculty of Science (FNWI) at the University of Amsterdam (UvA). You are also expected to spend part of your time at the Department of Multi-Actor Systems in the Faculty of Technology, Policy and Management at TU Delft, and to collaborate actively with the Netherlands eScience Centre.
What are you going to do
- Develop and scale up a spatially explicit agent-based model to systematically explore the factors reinforcing poverty traps as climate change intensifies.
- Perform sensitivity analysis of the model. Develop and implement strategies for model calibration and validation.
- Collaborate with researchers at Red Cross Red Crescent Climate Centre to explore a wide range of real-world scenarios using the agent-based model.
- Publish in high level international journals, presenting at leading conferences and collaborate with PhD/Master/Bachelor students.
- Assure research data management following the FAIR data principles.
- You will have the opportunity to contribute to policy reports and translating scientific evidence to practical recommendations.
What do we require of you
- PhD degree in Computer Science, Developmental Economics or Environmental Sciences and interests in the social sciences.
- Expertise in spatial agent-based modelling and good understanding of methods for model analysis and evaluation (e.g. global sensitivity analysis). Experience with programming agent-based models in Python.
- Background in the field of poverty traps, climate change or flood risk policy is an asset.
- Ability to work well within a multidisciplinary team.
- Good communicative skills in English, both written and oral. Please identify your three key publications in the motivation letter.
This is a companion discussion topic for the original entry at https://www.comses.net/jobs/543/