Research Assistant in Machine Learning for Epidemiology

Overview

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 contribute, for example, the burden of circulating viral infections. This project strives to develop an artificial intelligence (AI)-driven early warning system to predict the risk of Strep A outbreaks and upsurges before they occur. The RA will work to integrate multiple datasets and apply a combination of mathematical modelling methods, including time-series modelling and probabilistic machine learning. Anticipated work includes developing and training models, with validation and refinement steps using retrospective data and simulation work undertaken where appropriate. This will be followed by a further evaluation phase to assess model performance, usability, and potential for real-world application using prospective data.

Main duties and responsibilities

• Collate, clean and prepare epidemiological datasets from a range of sources, ensuring data quality.
• Apply and help develop machine learning models, including feature preparation, model selection, fitting, and hyperparameter tuning.
• Conduct exploratory data analysis and produce descriptive statistics and clear visualisations.
• Contribute to model evaluation and validation, including selecting appropriate performance metrics demonstrating model generalisability to the real world.
• Ensure all activities comply with data protection legislation, research ethics requirements, and information governance procedures for handling sensitive health data.
• Work proactively and on own initiative, responding constructively to feedback from the line managers / principal investigators.
• Carry out other duties, commensurate with the grade and remit of the post.

Person Specification

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.

Criteria | Essential or desirable | Stage(s) assessed at

An undergraduate degree in a quantitative discipline such as data science, statistics, computer science, mathematics, epidemiology or a closely related field . | Essential | Application

Demonstrable programming skills in Python, including common data science / machine learning libraries (eg pandas, scikit-learn or PyTorch/JAX). | Essential | Application/interview

Working knowledge of core machine learning methods (eg regression, classification, clustering). | Essential | Application/interview

Experience in cleaning, wrangling, and preparing structured datasets for analysis | Essential | Application/interview

Experienced in data visualisation and version control. | Essential | Application/interview

Good written and verbal communication skills. | Essential | Interview

Ability to work independently, prioritise and meet deadlines within limited weekly hours with minimal supervision. | Desirable | Interview

Awareness of data governance, confidentiality, and the ethical handling of sensitive health data. | Desirable | Interview

Further Information

Grade | Grade 6

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

Work arrangement | Part-time

Duration | Until 31st August 2027

Line manager | Academic Fellow in AI for Health and Clinical Research Fellow

Direct reports | None

Right to work in the UK | This role does not meet the criteria for Skilled Worker sponsorship under the UK Visa & Immigration points-based system. Information on alternative visa options is available on Work visas

Our website | sheffield.ac.uk/machine-intelligence

For informal enquiries about this job contact Dr Midhun Parakkal Unni, Academic Fellow in AI for Health, at M.ParakkalUnni@sheffield.ac.uk

Next steps in the recruitment process

It is anticipated that the selection process will take place in the week commencing 16th November 2026. This will consist of an interview with the project investigators. We plan to let candidates know if they have progressed to the selection stage in the week commencing 2nd November 2026. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact COM-Recruitment@sheffield.ac.uk

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).

What we offer

• A minimum of 38days annual leave, including bank holiday and closure days (pro rata), with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
• Recognition Awards to reward staff who go above and beyond in their role.
• A commitment to your development access to learning and mentoring schemes, integrated with our Academic Career Pathways.
• A range of generous family-friendly policies
• paid time off for parenting and caring emergencies
• access to menopause support in the workplace
• paid time off and support for fertility treatment
• and more

More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

We are a Disability Confident Leader (opens in a new window). If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.