Research Associate / Fellow (fixed term)

We are looking for a data modelling research fellow to develop and analyse multisystemic statistical and network models predictive of depression trajectory outcomes in young people who are not in education, employment or training (NEET), calibrate the network on our data, and use that network to identify those factors to which depressive outcomes are most sensitive. The data will include psychological, medical, environmental, social, cultural and economic factors, gathered from large cohorts of young NEETs in South Africa and Nigeria. The role is part of the R-NEET project, an international £5M Wellcome funded project led from the University of Pretoria. The role holder will work closely with other researchers on the project in wide ranging disciplines in the UK, Canada, Nigeria and South Africa.

The role holder should have PhD awarded or near successful completion (post viva) in a topic relevant to the project. They should have experience in the development and analysis of statistical and/or network models, with application in at least one of psychology, sociology, health science, environmental science or resilience. Skills in development, use and analysis of Causal Inference / Bayesian Network / Probabilistic Graph Theory models and/or other relevant statistical, machine learning or AI techniques are desirable and would be an advantage, as would experience of effective team working with empirical researchers in one of the listed application areas.
The role holder must have high proficiency in a relevant programming environment (e.g. R, Matlab or Python). Excellent English language oral and written communication skills are also essential, including the proven ability to author research articles, and to communicate complex ideas to non-specialists. The person we appoint will have an outstanding publication record commensurate with career stage and must be able to demonstrate commitment to the aims and values of the project in line with their longer term career development.
The post is offered on a full time (36.25 hours per week), fixed term contract, with an anticipated start date of 01 August 2025, funded for up to 4 years. The contract must end by 05 November 2029. The latest start date to ensure a 4 year contract will be 06 November 2025. Job share arrangements may be considered.