|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||£17,668 tax-free stipend per annum|
|Placed On:||23rd March 2023|
|Closes:||24th May 2023|
This PhD project will harness the power of developments in image analysis to solve the real-world challenge of estimating wind hazard. This is an exciting opportunity to apply state-of-the-art techniques (e.g. machine learning) to video clips of moving trees, and conduct corresponding fieldwork to develop new understanding of the natural environment and atmospheric processes at work. At the core of the work will be a campus-scale study, with a variety of CCTV camera data linked to data from the university’s weather station. Initially, the project will use data already collected in a pilot study and existing particle-tracking code to create a local (micro-scale) map of wind hazard. There is clear space for a student to excel by creating a step-change in the image analysis techniques. The opportunity also exists for wind tunnel experiments and physical modelling. The final aim is to create a proof-of-concept tool for high-resolution, low-cost mapping (i.e. high/low hazard areas) without installing specific technology such as wind meters, enabling site-specific recommendations on mitigation (e.g. tree planting as a shield).
This PhD project will be one of seven PhD projects being pursued in a cross-disciplinary mini-College of Doctoral Training (mini-CDT) running at Loughborough University from 2022 to 2026. This mini-CDT is part-financed and supported by the WTW Research Network, the award winning collaboration between academia, finance and research industries on the understanding and quantification of risk.
This industry collaboration builds on the earlier partnership between Loughborough University and WTW in the recently completed TECHNGI research project on technology and next generation insurance services. A full description of the mini-CDT, of the individual PhD projects and of the support provided by the WTW Research Network is here.
Primary supervisor/CDT lead: Dr John Hillier
Secondary supervisors: Dr Sarah Bugby and Dr Tim Marjoribanks
Applicants should have or expect to achieve at least a 2:1 Honours degree (or equivalent). A relevant master’s degree or work experience would be advantageous.
We encourage any applicants with a keen curiosity about the natural environment and technical competence in data handling (e.g. data analysis, image processing, big data). Backgrounds in a variety of fields are suitable e.g. Environmental science, Physics, Geography, Engineering, Computer Science, Data Science.
How to apply
All applications should be made online. Under programme name, select Geography and Environment.
Please quote the advertised reference number: GE23-JH-CDT in your application.
Please upload with your application the following supporting documents:
To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.
Interviews are due to take place June.
The studentship is for 3 years and provides a tax-free stipend of £17,668 per annum for the duration of the studentship plus tuition fees at either the UK or International rate.
Type / Role: