Postdoc in Modelling the safe operating space of the dry tropics


University of Edinburgh


Thursday, September 23, 2021

Start Date

Monday, November 1, 2021

We seek a 3-year post-doctoral research associate (PDRA) with expertise in biogeochemical modelling, data assimilation and the terrestrial carbon cycle. The post is a core part of the SECO project, which seeks to quantify the carbon cycle of the dry tropics (

SECO is a 5-year NERC Large Grant which will combine field observations in long term plots, remote sensing, data assimilation and biogeochemical modelling to answer key questions about the size, location and future of the carbon sink in tropical savannas and dry forests. SECO is a global partnership of 22 organisations including, in the UK, the Universities of Edinburgh, Leeds, Sheffield and Exeter, and with collaborators across the global dry tropics.

We seek a PDRA with expertise in simulation modelling to lead on the development and calibration of a robust process model of C cycling across the dry tropics. This work will identify key processes that underpin regional variations in carbon cycling, including tree-grass interactions and disturbance regimes. You will also determine the uncertainties that limit model forecast accuracy and work closely with the SECO data team to resolve and reduce these uncertainties. Using the calibrated model, you will then address two key science questions: (1) How strong are the current feedbacks that maintain tree-grass coexistence in the dry tropics? (2) What is the safe operating space for biomass in the dry tropics? The safe operating space is one that avoids homogenisations due to tree or grass dominance. The successful PDRA will join a team including research staff with expertise in plot measurements, remote sensing and data science, a project manager, field technicians and several PhD students. We are looking for someone who is motivated by the prospect of synthesising multiple information sources to understand the complex carbon cycle of the dry tropics and to find robust ways to predict its future under global change.