Research Assistant in Machine Learning for Epidemiology

The University of Sheffield; Sheffield; Hybrid; GBP 32,000 - 36,000; Part time

Benefits offered by this job

Flexible working

Generous pension scheme

Extensive annual leave

Discounts and staff networks

Job summary

The University of Sheffield invites applications for a part-time Academic Fellow in AI for Health to integrate datasets and develop time-series and probabilistic models. The role focuses on training and evaluating models with retrospective data and simulated scenarios.

Working hours are limited weekly, with a pro-rata salary. Applicants should have a quantitative degree, Python skills, and experience in ML methods, data cleaning, and visualization.

Qualifications

• An undergraduate degree in a quantitative discipline such as data science, statistics, computer science, mathematics, epidemiology or a closely related field.
• Demonstrable programming skills in Python, including common data science libraries (pandas, scikit-learn or PyTorch/JAX).
• Working knowledge of core machine learning methods (eg regression, classification, clustering).
• Experience in cleaning, wrangling, and preparing structured datasets for analysis
• Experienced in data visualisation and version control.
• Good written and verbal communication skills.
• Ability to work independently, prioritise and meet deadlines within limited weekly hours with minimal supervision.

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.

Skills

Python

Pandas

scikit-learn

Machine learning

Data wrangling

Data visualization

Version control

Communication

Independent work

Education

Undergraduate degree in a quantitative discipline

Tools

Python libraries

Github

Job description

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. Find out more about our benefits (opens in a new window) and join us to become part of something special.

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.

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

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 www.gov.uk

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 [e-mail].

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 [e-mail]

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 38 days 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.

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision (www.sheffield.ac.uk/vision) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.