PhD Studentship: STEM, Maths, Physics, Computer Science: Principles and Methods to Support Data-informed Decisions in Olympic and Paralympic Sport

A large volume of data are routinely captured in high-performance training and competition environments. Whilst these data have the potential to inform key performance decisions, the full potential performance impact is often not realised. In some instances, this is because the volume of usable data is relatively small – compared with other ‘big data’ domains – due to factors such as data quality and/or completeness. In other instances, where sufficient data do exist, the models used in attempts to glean performance insight may be overly simple and violate mathematical or statistical assumptions or be overly complex and fail to fully reflect applied sporting principles or be interpretable and usable for practitioners and coaches. 

This project aims to address genuine performance questions in Olympic and Paralympic sports by firstly mapping their performance‑critical decisions and identifying the information, evidence and conditions required to make those decisions well. Secondly, the project will implement bespoke methods – including advanced data modelling approaches (e.g., machine learning, digital twin models) and AI techniques where appropriate – to provide novel solutions that enable sports to make performance decisions that are better informed by data and to make performance‑critical decisions with greater confidence and clarity. This is an applied data science collaboration between Swansea University’s Department of Sport and Exercise Sciences (Sports Analytics specialist area) and the UK Sports Institute, and it will combine expertise from both partners in sports science, mathematics and statistics, and computer science. We welcome applications from candidates with a passion for high-performance sport, education and experience in any/all of the above, and a willingness to extend their skills in, and collaborate with experts from, other disciplines.

Funding Comment: Covers full tuition, £20,780 stipend (2025/26), plus up to £1,000 yearly for research costs.

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