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Hayley Fowler

1 year ago

PhD Studentship: NERC Resilient Flood Futures (FLOOD-CDT): A sub-hourly quality controlled blended UK precipitation dataset to understand uncertainty in flood predictions Newcastle University in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD Project (Students Worldwide)

Deadline

Expired

Country flag

Country

United Kingdom

University

Newcastle University

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Keywords

Computer Science
Data Science
Machine Learning
Environmental Science
Mathematics
Hydrology
Civil Engineering
Earth Science
Uncertainty Analysis
Radar Technology
Quality Control
Earth Observation
Climate Dynamics
Climate Resilience
Flood Risk
Infrastructure Resilience
Precipitation
Statistic

About this position

This fully funded PhD studentship at Newcastle University, sponsored by the Natural Environment Research Council (NERC), addresses the urgent need for reliable, high-resolution rainfall data to improve flood prediction and climate adaptation in the UK. Climate change is causing more frequent and intense rainfall events, challenging current flood risk management strategies. Existing rainfall datasets are limited: radar estimates can be inaccurate, and rain gauge networks are too sparse to capture localised storms.

The project aims to develop a new blended UK precipitation dataset with high temporal (15-minute) and spatial resolution by integrating data from tipping bucket rain gauges, weather radar, and satellites. The student will employ robust coding and data science techniques to build a comprehensive quality control framework, identify and correct errors, apply bias adjustments, and assess data quality. Advanced multisource blending methods, such as kriging, probabilistic merging, and machine learning, will be used to combine datasets and preserve extremes. Uncertainty will be explicitly quantified, and the resulting open-access dataset will support advanced flood risk modelling, hydrological predictions, and adaptation planning.

The student will join Newcastle University's Water and Climate Research Group and collaborate with partners like the UK Environment Agency and the Floods and Droughts Research Infrastructure (FDRI). The project offers direct pathways to impact in policy, industry, and society, and provides training in climate extremes, data science, and hydrology. Applicants must hold or expect to obtain at least a 2:1 Honours degree (or international equivalent) in a relevant subject, such as computing, mathematics, or engineering. Strong analytical, communication, and independent research skills are essential.

International applicants are welcome and must meet English language requirements (IELTS 6.5 overall, minimum 5.5 in all sub-skills) and may require ATAS clearance. The studentship covers 100% tuition fees, a minimum annual living allowance of £20,780 (2025/26 UKRI rate), and additional project costs. The application deadline is January 8, 2026.

To apply, candidates must use the Newcastle University portal, select the appropriate programme, and provide a personal statement, CV, academic transcripts, degree certificates, language certificate (if applicable), and two academic referees.

Funding details

Funded PhD Project (Students Worldwide)

What's required

Applicants must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a relevant subject (including computing, mathematics, engineering, etc.). Essential requirements include enthusiasm for research, independent thinking, excellent analytical skills, and strong verbal and written communication skills. Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills. International applicants may require ATAS clearance prior to obtaining a visa. Two academic referees, academic transcripts, degree certificates, and a CV are required.

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Professors

Professor at Newcastle University

University Name
Country flag

United Kingdom

Research Interests

Hydrology

Statistics

Environmental Science

Earth Science

Climate Dynamics