Sports and Exercise Science: Fully Funded EPSRC INEOS PhD: Towards the Development of Digital Twins for Efficient Talent Identification in Professional Cycling

Funding providers: EPSRC and INEOS

Subject areas: STEM, Mathematics, Physics, Computer Science, Sports Science

Project start date: 1 October 2025 (Enrolment open from mid-September)

Supervisors: Prof Liam Kilduff (Joint Primary Supervisor), Dr Rowan Brown (Joint Primary Supervisor), Prof Neil Bezodis (Supervisor Team), Dr Mark Waldron (Supervisor Team), Dr Laura Mason (Supervisor Team), Dr Scott Drawer (Industrial Supervisor)

Aligned programme of study: PhD in Sports Science

Mode of study: Full-time – location will be a combination of Swansea University, INEOS and other key data partners

Project description:

The landscape of professional cycling has changed dramatically over the past decade with more nations represented at a professional level. This has led to more professional teams introducing development teams (U23s), and increasingly more teams are now considering riders at the junior level to ensure a pipeline of talented riders.

The identification of talent at younger ages is becoming increasingly competitive; more efficient and effective methods are needed. Cycling remains one of the more technologically advanced sports and various forms of performance and race data are often publicly available. However, these data sources are vast and sometimes inconsistent - automated methods would enhance the collation and analysis of data to better identify talent that can consequently be invited for further testing.

The aim of this project is therefore to design and build a digital thread. This will utilise current and legacy INEOS performance metrics and associated public data streams to identify and track the trajectory of junior level riders across the world. Initially, techniques will be designed to follow national junior results across all competition formats in a select number of countries and build automated systems to flag ‘unusual and interesting’ results. This will then allow more in-depth exploration of individual training history and trajectories over time.

Eligibility

2:1 (or above) in STEM

It would be desirable for candidates to have experience in some or all of the following:

• Data pipelines and database architecture
• Private and public cloud data platforms (i.e. Azure, GCP, AWS)
• A variety of programming languages (i.e. R, Python, SQL) required
• Data visualization solutions (i.e. Power BI, Tableau) for producing reports

IELTS 6.5 Overall (with no individual component below 6.5) or Swansea University recognised equivalent.