PhD - Seeing extreme winds: machine learning applied to video to better estimate site-specific wind hazard

European Geosciences Union (EGU); Loughborough; On-site; GBP 20,000 - 24,000; Full time

Benefits offered by this job

RTSG grant

Tuition fees covered

Job summary

Loughborough University invites applications for a PhD studentship exploring extreme winds using machine learning applied to video clips to better estimate site-specific wind hazard. The project runs 3.5 years from October 2027 with a tax-free stipend and tuition fees covered.

You will install cameras and anemometers, develop ML/video analysis pipelines, and link results to historical data to inform hazard mapping for potential national scale use.

Qualifications

• Master's degree in a relevant field or equivalent.
• Strong background in data analysis and ML is required.
• Experience with Python/R for scientific computing is preferred.

Responsibilities

• Set up a campus-scale living lab with cameras and anemometer sets for at least a year.
• Apply ML, computer vision and physics-based modelling to estimate wind gusts from video.
• Integrate video data with historical weather data (ERA5) and build a wind hazard map.

Skills

Machine learning

Image processing

Python

R

Fieldwork

Data analysis

Education

Master

Tools

Python

R

Job description

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Position

PhD - Seeing extreme winds: machine learning applied to video to better estimate site-specific wind hazard

Employer

Loughborough University

Loughborough has been one of the UK's top ten universities for ten years now.

Homepage: https://www.lboro.ac.uk/

Location

Loughborough, United Kingdom

Sector

Academic

Relevant divisions

• Atmospheric Sciences (AS)
• Geosciences Instrumentation and Data Systems (GI)
• Natural Hazards (NH)

Type

Full time

Level

Student / Graduate / Internship

Salary

• The studentship is funded for 3.5 years starting from October 2027 and provides a tax-free minimum stipend of £21,805 per annum (from 1 October 2026) for the duration of the studentship plus tuition fees at the UK rate (£5,238 in 2026/27)

Preferred Education

Master

Application deadline

6 January 2027

Posted

11 November 2026

Job Description

The PhD advert: https://centa.ac.uk/studentship/2027-lu02-seeing-extreme-winds-machine-learning-applied-to-video-clips-to-better-estimate-site-specific-wind-hazard/

Extratropical cyclones such as Storm Eunice inflict extreme winds on the UK, causing billions of pounds in insured losses. Yet the peak 3-second gusts that drive damage vary greatly over short distances in cities, and weather stations and models cannot capture this. This PhD tackles the problem with visual anemometry: extracting gust speeds from short video clips of trees.

Building on pilot work showing that optical flow tracking of tree motion captures peak gusts, you will set up a campus-scale "living lab" at Loughborough University, installing four to five camera and anemometer sets for at least a year. You will combine this dataset with machine learning, laser scanning and a physics-based digital twin of trees to test how well video measures wind, even in extreme conditions. You will then link these small-scale observations to historical weather data such as ERA5 and show how buildings shelter or expose sites.

The work collaborates with the ArboKlim project (Prof. Theresa Blume), which is instrumenting trees in Potsdam, Germany.

The aim is a low-cost, proof-of-concept hazard-mapping tool. It could scale nationally using CCTV networks and inform the (re)insurance sector.

Training covers machine learning, image processing, Python/R, applied meteorology and fieldwork.

• The studentship is funded for 3.5 years starting from October 2027 and provides a tax-free minimum stipend of £21,805 per annum (from 1 October 2026) for the duration of the studentship plus tuition fees at the UK rate (£5,238 in 2026/27) and a Research Training Support Grant (RTSG) of £8,000. Further guidance about eligibility is available at UKRI Terms and Conditions. Excellent international students (including EU) are eligible to apply and there will be a fee waiver in place covering the difference between Home and International fees for one successful candidate.