We are recruiting two postdocs within the MuSES and GRAID projects for social-ecological modeling of traps and cross-scale interactions in small-scale fisheries and agriculture. More details about the positions can be found here. The deadline for application is Dec 7th, 2016.

MuSES is a new interdisciplinary five year project of the SES-LINK group aiming to build theory of the co-evolutionary dynamics of social-ecological systems (SES), with a particular focus on cross-scale interactions. It combines theoretical and empirically-based modelling approaches to investigate how micro-level interactions between humans and the biosphere give rise to observed macro-level SES phenomena such as resource collapses and to identify critical social-ecological mechanisms for sustainable aquatic and terrestrial food production systems. The project is funded by an ERC consolidator grant to Maja Schlüter.

SRC’s GRAID program (Guidance for Resilience in the Anthropocene: Investments for Development) is a long-term collaboration to build on SRC research in the theory and practice of resilience for development. GRAID will generate knowledge and synthesize insights on resilience thinking, and approaches for assessing and building resilience in the context of development. GRAID is funded by Sida and has been developed as a strategic knowledge partner to the Global Resilience Partnership (GRP) which is convened by The Rockefeller Foundation, USAID and Sida.

The work of the postdocs will advance theoretical and methodological research on the dynamics of SES. Theoretical work will involve the development of stylized dynamical models to investigate interactions and feedbacks that underlie observed social-ecological phenomena, such as poverty traps or resource collapses. The stylized models are intended to support the development and testing of hypotheses about human-environment relationships that determine SES outcomes and enhance understanding of general patterns and causal mechanisms. They will be based upon and compared against general patterns observed in real-world settings.