This is an exciting opportunity for a Researcher to conduct primary research with multimodal, high resolution spatiotemporal data from a low-income setting. You will be a member of an international partnership that is employing cutting-edge big data methodologies for the study of a disease of poverty. You 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. We are looking to appoint a highly motivated Researcher to conduct research on force-of-infection models using spatiotemporal data at the level of an individual water site where transmission of schistosomiasis occurs. You will join an interdisciplinary team of field epidemiologists, mathematicians, machine learning scientists, technicians, parasitologists, global health ethicists, and health practitioners.

You will manage and analyse complex datasets from the SchistoTrack Cohort, involving data from remote sensing and wearable GPS loggers as well as waypoint data from households, villages, schools, and health centres. Your main objective will be to develop methods to classify water contact sites based on individual usage and contamination to understand whether there are focal points for transmission or super spreaders. You will build on environmental models developed within the group to construct force-of-infection models for parasite acquisition at water/transmission sites. This requires combining environmental determinants with human behavioural exposures. The role does not involve overseas travel unless of interest and requires someone 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 geostatistics, spatial analysis, complex network science, machine learning, 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.

Applications for this vacancy are to be made online and require a supporting statement and CV. The supporting statement must address the selection criteria for the post using examples of your skills and experience.