This is an exciting opportunity to conduct primary research with multimodal biomedical 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 schistosomiasis, a disease of poverty. The candidate will join the Nuffield Department of Population Health, based within the Big Data Institute at the University of Oxford.

The candidate will report directly to the Group Head, Associate Professor Goylette Chami. The role will involve providing statistical expertise to a multi-disciplinary team by creating mathematical models, developing dynamic simulations, and performing Bayesian analyses of complex morbidities across multiple time points. You will be part of a team of approximately 15 individuals at Oxford, including students, postdocs, data scientists, and administrators. The Oxford team works across statistical, machine learning, and image analysis methods. This post provides an exciting opportunity to work with high-dimensional datasets with biomedical, socioeconomic, and spatial information to address a disease of poverty.

The postholder will develop and apply statistical solutions to build models for the complex aetiologies of schistosomiasis-associated morbidities, focusing on hepatic schistosomiasis. This work will need to account for responses to treatment and aspects of time such as onset and duration. The position is a great opportunity for someone with an advanced quantitative background, who would like to work with challenging methodological problems as part of a large research team and project with primary data collection on infectious diseases. We are particularly interested in someone with advanced experience in Bayesian statistics who is willing to learn schistosomiasis pathology and epidemiology.

You will hold a PhD/DPhil in statistics, epidemiology, mathematical modeling, or a related scientific discipline and have experience of advanced statistical/computational skills using R with relevant analysis packages to analyse temporal data.

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.

Informal enquires are encouraged and should be addressed to Dr Goylette Chami (Goylette.chami@ndph.ox.ac.uk). Further particulars, including details of how to apply, can be obtained from the document below.

Only applications received by 12.00 midday on 1st June 2023 will be considered.