Hayley Fowler
3 months ago
PhD Studentship: 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
Available
Deadline
Expired
Country
United Kingdom
University
Newcastle University

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Where to contact
Official Email
Keywords
About this position
Project Overview: Climate change is intensifying extreme rainfall events, posing significant challenges for flood risk management in the UK. Existing rainfall datasets are either too sparse or inaccurate, making it difficult to predict floods and plan for climate adaptation. This fully funded PhD studentship at Newcastle University aims to address this gap by developing a high-resolution, sub-hourly blended UK precipitation dataset. The project will integrate data from multiple sources, including tipping bucket rain gauges, weather radar, and satellites, using advanced coding and data science techniques.
Research Focus: The student will design and implement a comprehensive quality control framework to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods such as kriging, probabilistic merging, and machine learning will be used to combine datasets and preserve rainfall extremes. The project will explicitly quantify uncertainty, with outputs tailored for both researchers and practitioners. The resulting open-access dataset will directly support advanced flood risk modelling, hydrological predictions, and climate adaptation planning.
Training and Collaboration: The successful candidate will join Newcastle University’s Water and Climate Research Group, collaborating with partners such as the UK Environment Agency and the Floods and Droughts Research Infrastructure (FDRI). The studentship offers the opportunity to develop expertise in climate extremes, data science, and hydrology, with direct pathways to impact in policy, industry, and society.
Funding: The award covers 100% of tuition fees and provides a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs are also covered. Both home and international applicants (including EU) are eligible for the full studentship.
Eligibility: Applicants must have, or expect to obtain, at least a 2:1 Honours degree or international equivalent in a relevant subject (e.g., computing, mathematics, engineering). Essential skills include enthusiasm for research, independent working, analytical ability, and strong communication skills. Non-native English speakers require an IELTS score of 6.5 overall (minimum 5.5 in all sub-skills). International applicants may need an ATAS certificate for visa purposes.
Application Process: Applications should be submitted via the Newcastle University funding page. A prospective applicant webinar will be held at 2:00pm on 26th November (see event link for details). For further information, contact Prof Hayley Fowler at [email protected].
Key Dates: Application deadline is 8th January 2026. The studentship will commence on 1st October 2026 and will last for 3.5 years.
Funding details
Available
What's required
Applicants must have, or expect to gain, at least a 2:1 Honours degree or international equivalent in a relevant subject such as computing, mathematics, engineering, or a related field. Essential requirements include enthusiasm for research, the ability to work independently, excellent analytical skills, and strong verbal and written communication skills. Applicants whose first language is not English must have an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills. International applicants may require an ATAS clearance certificate prior to obtaining a visa.
How to apply
Click the 'Apply' button on the Newcastle University funding page to submit your application. Ensure you meet the eligibility criteria and prepare all required documents. For further information, attend the prospective applicant webinar or contact Prof Hayley Fowler at [email protected].
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