Research Assistant in Machine Learning for EpidemiologyUniversity

Could machine learning help us see a Strep A outbreak coming before it arrives? That's the question at the heart of this project, and we're looking for a Research Assistant to help answer it.

Strep A is a common bacterial infection that causes spectacular, unexpected outbreaks of disease every few years. The cause of these outbreaks is unknown, but several factors are likely to chip in, for example, the burden of circulating viral infections. Our goal is to predict the risk of Strep A outbreaks and upsurges before they occur, giving clinicians and public health teams precious time to respond.

You'll bring together multiple real-world datasets and develop and evaluate machine learning models that turn them into early-warning signals. You'll also collaborate with clinicians and an engineering team, so your analysis feeds directly into a tool with real potential to protect people.

What we're looking for

You'll hold a good degree in a quantitative subject — data science, engineering, statistics, computer science, mathematics, epidemiology or similar — and be a confident programmer in Python and/or R. You'll have a solid grasp of core machine learning methods, care about reproducible analysis, and enjoy working across disciplines. Experience with health data, time series or mathematical modelling is welcome, though curiosity and strong fundamentals matter most.

Why Sheffield

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits, including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more.

£32,080 pro rata (equivalent to a full-time salary), with an actual salary of £6,416 based on seven hours a week.