This is an exciting opportunity for a scientist to conduct primary research with spatiotemporal data from a low-income setting as member of an international partnership that is employing cutting-edge big data methodologies for the study of a disease of poverty. The candidate will join the Big Data Institute and Nuffield Department of Population Health, both of which are home to world-renowned research groups applying quantitative methods to key global health issues. For this post, we are looking to appoint a highly motivated scientist to conduct research on rural Ugandan individual mobility patterns and schistosomiasis exposures. You will join an interdisciplinary team of field epidemiologists, mathematicians, machine learning scientists, technicians, parasitologists, global health ethicists, and health practitioners.

Reporting to Dr Goylette Chami, the candidate will manage and analyse complex datasets from the SchistoTrack Project, involving data from wearable GPS loggers as well as waypoint data from households, villages, schools, and health centres. In addition, high dimensional water activity data will be available from household surveys and direct observations of water contact. The key objective is to develop methods and indicators for using spatiotemporal big data to understand transmission for schistosomiasis. The role does not involve overseas travel unless of interest, and requires a researcher with a strong quantitative/computational background with the capability to learn schistosomiasis epidemiology. Your responsibilities and duties will include to support and collaborate with other group/project members and Ugandan researchers. You will publish research articles for leading peer-reviewed journals and present papers at flagship conferences or meetings, as well as participate in community/public engagement activities.

You will hold or be close to completion of a PhD/DPhil in epidemiology, health data science, statistics, spatial analysis, complex network science, or a related scientific discipline. You will have demonstrated the ability to manage your own academic research and associated activities. A strong track record of advanced statistical skills and experience with large-scale spatial, temporal, or spatiotemporal data are essential.

The post is full-time (part time considered) and fixed-term until the end of March 2023 in the first instance.

Informal enquires are encouraged and should be addressed to Dr Goylette Chami (Goylette.chami@ndph.ox.ac.uk)..

Only applications received by 12.00 midday on 25th March 2022 will be considered.