The WorldPop programme, based at the University of Southampton, is seeking to recruit two researchers to assist with spatial demographic analyses in a number of low-income countries around the world. We require enthusiastic statistical analysts to work on a range of research projects relating to high-resolution predictive mapping of population distributions and demographics based on household survey, census and bespoke survey data, along with high-resolution geospatial covariate data layers. The work will be foundational to a new collaborative programme entitled GRID3 (Geospatial Reference, Infrastructure and Demographic Data for Development) with partners at National Statistics Offices, the Flowminder Foundation, the Bill and Melinda Gates Foundation, DFID, CIESIN and UNFPA, and will be used to guide projects ranging from population enumeration to delivery of health programs and alleviation of poverty. As well as joining the vibrant and well-connected WorldPop and Flowminder Foundation teams, you will have opportunities to lead high-impact publications.

The project work to be undertaken will include some or all of the following:

1. Developing spatial statistical approaches, including uncertainty quantification, for the production of high resolution gridded estimates of population counts and demographic characteristics in the absence of national census data across countries, through the integration of geospatial covariate layers and survey data
2. Contributing to the design of sampling strategies to obtain additional survey data
3. Responding to ad-hoc analysis and modelling requests from governments and agencies
4. Supporting national statistical offices in the analysis of existing census data or future census planning
5. Contributing to the development and delivery of workshops aimed at training outside staff (i.e. at national statistics offices, international agencies) in the methods developed

You will have a PhD *or equivalent professional qualifications and relevant industry experience in a statistical/computational/quantitative discipline strong experience in spatial statistical analysis and computer programming skills would be advantageous.. The project work will be highly interdisciplinary and as such there is some flexibility to accommodate expertise from a range of cognate disciplinary backgrounds (e.g. geography, demography, statistics, computer science, ecology, epidemiology etc).  A willingness to travel overseas occasionally is also required.