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PhD in Machine Learning for Early Warning of Debris Screen Blockage in Urban Rivers University of Bath in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Competition-funded PhD studentship. Studentships cover tuition fees, provide a £1,000 per annum training support fee, and include a maintenance stipend of £21,805 at the 2026/27 rate. Home and Overseas students may be considered, but Overseas awards are capped under UKRI policy. International students must cover Student visa and Immigration Health Surcharge costs from personal funds.
Country
United Kingdom
University
University of Bath
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About this position
Bath Engineering & Design Studentships at the University of Bath are offering a PhD project on machine learning for early warning of debris screen blockage in urban rivers. The project sits in the Department of Architecture & Civil Engineering and is led by Prof Thomas Kjeldsen.
This research aims to develop new methods for forecasting debris and trash screen blockages in urban rivers, moving from reactive maintenance to proactive flood risk management. The project combines artificial intelligence, computer vision, data science, hydrology, hydraulic modelling, and urban flooding. It will analyse CCTV image archives, meteorological datasets, rainfall forecasts, catchment descriptors, and hydraulic models to build a scientific framework for predicting blockage risk and its downstream flood implications.
The work is especially relevant to students interested in image-based machine learning, forecasting, flood risk, and infrastructure resilience. Research themes include identifying blockage conditions from images, transferring models between sites with limited data, combining weather and catchment information with image-derived signals, and coupling probabilistic forecasts with flood models.
Funding is available as a studentship. The award covers tuition fees, includes a £1,000 per annum training support fee, and provides a maintenance stipend of £21,805 (2026/27 rate). Home and Overseas applicants may be considered, although Overseas awards are capped. International students must fund their Student visa and Immigration Health Surcharge themselves.
Applicants should have, or expect to obtain, a UK 1st or 2.1 honours degree or equivalent; a master’s degree would be advantageous. Experience or strong interest in applying machine learning to technical problems is required, and non-UK applicants not currently studying in the UK must meet the English language requirement before the deadline.
To apply, submit the University of Bath online PhD application for Civil Engineering. Use the exact project title, name Prof Thomas Kjeldsen as intended supervisor, and select University of Bath URSA in the funding section. If applying for two projects, complete the second-project fields as instructed in the advert.
Funding details
Competition-funded PhD studentship. Studentships cover tuition fees, provide a £1,000 per annum training support fee, and include a maintenance stipend of £21,805 at the 2026/27 rate. Home and Overseas students may be considered, but Overseas awards are capped under UKRI policy. International students must cover Student visa and Immigration Health Surcharge costs from personal funds.
What's required
Applicants must have, or be about to obtain, a UK Honours degree at 1st or 2.1 level, or an international equivalent. A master’s-level qualification would be advantageous. Applicants should have interest and experience in applying machine learning techniques to technical problems. Non-UK applicants who are not currently studying in the UK must meet the programme’s English language requirement before the application deadline.
How to apply
Apply through the University of Bath online PhD application form for Civil Engineering. Enter the project title exactly as given and name Prof Thomas Kjeldsen as the intended supervisor. If applying for two projects, include the second project title and supervisor in the designated fields, and select University of Bath URSA in the Funding Your Studies section.
More information can be found here
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