Applicants are invited to undertake a 3 year PhD program in the Department of Physics and Astronomy at the University of Kent in partnership with Body Rocket Ltd. The project aims to develop machine learning algorithms for real-time monitoring of aero dynamic drag, allowing cyclists to adjust their on-bike position dynamically for optimal performance. A fully funded studentship is available through the SEPnet SME-Doctoral Training Network.
Background
Cycle manufacturers have invested heavily in the development of aerodynamic packages with the aim of increasing a rider’s speed for the same amount of physical effort. However, 80% of aerodynamic drag is created by a cyclist’s body position, clothing, and the way they move as they ride.
Until now, the only way to accurately measure a cyclist’s aerodynamic drag was to pay for an expensive test session in a wind tunnel or at a velodrome.
Even then, the results obtained in a lab environment are likely to vary significantly from those experienced on the open road; with variable terrain, conditions, and fatigue setting in. Techniques currently applied to assess aerodynamics during field training are limited to tests carried out under specific, controlled conditions.
Body Rocket is a game-changing device that fits on a bike, with multiple sensors measuring your drag force in real-time, on any ride without particular constraints.
Body Rocket continue to perform extensive field trials of their product, acquiring very large datasets of variables relating to the intrinsic (e.g. distribution of forces & moments on the bike, weight variations) and extrinsic factors (e.g. wind speed, terrain slope) that affect drag (and hence performance). This project aims to analyse this data with the aim of providing real-time feedback for athletes, enabling dynamic adaptation of body position and pacing strategy. The research will include:
• Multivariate data reduction, visualisation and interpretation.
• Inference of body position using measurements from force sensors in the Body Rocket system and external parameters that characterise the course and environmental conditions.
• Design of dynamic pacing strategies through bio-feedback providing the cyclist with continuous instructions for achieving optimal body position and power output throughout an event.
The South-East Physics Network (SEPnet) comprises nine universities working together to deliver excellence in physics. The aim of the SME Doctoral Training Network (SME-DTN) is to create a critical mass of research to support both regional industries and national science priorities with funding from Research England Development Fund (RED). The SME-DTN aims to attract applications from diverse backgrounds and non-traditional routes.
All students recruited to the SME-DTN are expected to attend the Graduate Network Summer and Winter Schools and up to 5 advanced physics courses during the course of their PhD. For details of the GRADnet Training Programme 2021-22 see here.
Applicant credentials
• First or good upper second class degree in Physics/Computer Science/Engineering or other relevant scientific discipline.
• First rate analytical and numerical skills, with a well-rounded academic background.
• Prior experience of data acquisition and analysis.
• Demonstrable programming experience, preferably in Python.
• A driven, professional and self-dependent work attitude is essential.
• Experience of working within industry will be an advantage.
• The ability to produce high quality presentations and written reports.
Funding
The studentship covers tuition fees, a tax-free stipend of £15,609 p.a. and an additional financial study package including travel budget, provided by the Research England Development Fund. Only UK Higher Education “Home Fee” status applicants are eligible.
Deadline
31 January 2022 23:45 (GMT)
How to apply
Apply through the University of Kent on-line system.
https://www.kent.ac.uk/courses/postgraduate/212/physics
Select the PhD in Physics option, with an entry date of May 2022 or September 2022 depending on your soonest available start date.
State that you wish to be considered for studentship reference SEPnet/SG/2022.
We advise early application as the position will be filled as soon as a suitable applicant can be found.
Due to the high volume of applications received, you may only hear from us if your application is successful.
Interested in this studentship?
Contact Dr Stuart Gibson, University of Kent for further information about this project: [Email Address Removed]
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