University of Sheffield

Below is a list of prospective projects you can apply for, complete with a short explanation and the lead supervisor's name and department. Please contact them before making your application; you will be asked if you have done this on the application form.

If you are interested to learn more about a specific project before applying, download the full project proposal at the bottom of the page.

A HPC-Powered ML Pipeline for Digital Mobility: Bridging Python & R-Shiny. Vitaveska Lanfranchi, Computer Science

This project addresses a technical gap in the implementation of real-world digital mobility monitoring in clinical decision-making. The intern will develop an integrated web pipeline which ingests sensor-based Digital Mobility Outcomes (DMOs) generated by the established mobgap Python library. By building a high-performance bridge to a visualisation tool, alongside machine learning models, the project will create a tool with potential to provide real-time monitoring and personalised diagnostic support. This will turn complex biomechanical signals into accessible, actionable insights for digital health monitoring.

Improving the FLAME GPU User Experience through Examples. Paul Richmond, Computer Science

FLAME GPU is a high-performance, open-source framework for simulating millions of interacting agents using GPU acceleration. This internship will expand accessibility by porting foundational models from NetLogo, with a focus on Social Sciences and Humanities applications. The proposed work lowers barriers to high-performance computing by showcasing the Python interface and delivering ready-to-use example models for researchers. Models will be benchmarked on the Bede supercomputer, with results targeted for NVIDIA’s Technical Blog. The initiative aims to broaden FLAME GPU adoption.

AI for Smart Farming: Detecting and Identifying Cattle from Drone Images. Mingqi Gao, School of Computer Science

Artificial intelligence and computer vision are increasingly being used to support smart farming and livestock management. This project explores how deep learning can be used to detect and identify cattle from drone images. Using real-world agricultural image data, the student will build and evaluate a prototype pipeline for cattle detection and identification. The project will provide hands-on experience with core machine learning and computer vision techniques, including image processing, neural networks, and model evaluation.

Upsk-AI-lling: Mapping skills in higher and further education with AI. Denis Newman-Griffis, Senior Lecturer and Theme Lead in AI-Enabled Research, School of Computer Science / Centre for Machine Intelligence

Higher and further education are vital pathways for skill development, but sector-level understanding of the skills provided by the enormous variety of courses available is difficult to achieve. This project, funded by the British Academy and the University of Sheffield, is developing an LLM-driven platform for analysing at scale the skills described in higher education, further education, and apprenticeship training courses, and mapping these to professional occupations and industrial sectors. The N8 CIR internship will enable scaling this analysis to all subject areas nationally, and facilitating on-demand analysis of new courses.

If you designed a project with a supervisor, please complete the application form with your project title.

This is what is required to complete the application form.

1. Your details
2. A prepared PDF document to upload containing:

• A two page CV
• The names of no more than two internship projects you are interested in applying for
• A short statement outlining your motivation and relevant experience for each project.

We expect this document to be no longer than 3 pages of A4.

Your uploaded file should be named: "YourSurname_initials_Sheffield.pdf" e.g., Other_AN_Sheffield.pdf

If you are having problems with your application, please contact us at enquiries@n8cir.org.uk

Applications will be assessed by a panel and short-listed candidates will be invited for interview.

Application deadline: April 2, 2026

Shortlisted candidates notified: April 15, 2026

Interview dates: w/c April 20, 2026

Successful students notified: w/c 27 April, 2026