AI-Powered Uncertainty-Aware Adaptive Methods for Property Risk Assessment in Climate Change Adaptation and Disaster Resilience (Ref: IRISK-26-LU-08)

About the Project

This project develops novel AI methods for property risk assessment under climate and disaster-related hazards, addressing challenges of fragmented data, uncertainty, and passive data use. It investigates uncertainty-aware multimodal data fusion and decision-theoretic data acquisition strategies to reduce uncertainty in risk estimation. By combining distributed digital data sources with satellite observations and leveraging computer vision techniques, the research enables adaptive and dynamically updateable risk assessment. A scoped predictive case study demonstrates the value of these methods. Co-designed with industry, the project supports improved decision-making for insurers and policymakers, contributing to climate change adaptation and disaster resilience.

Name of primary supervisor/CDT lead:
Baihua Li b.li@lboro.ac.uk
https://www.lboro.ac.uk/departments/compsci/staff/baihua-li/

Name of secondary supervisor:
Huili Chen

Entry requirements:
Applicants must already have, or expect to shortly graduate with, a very good undergraduate degree or Master’s degree (at least a UK 2:1 honours degree) – or an equivalent international qualification from a high ranking university – in a relevant subject. EU and Overseas applicants should achieve an IELTS score of 6.5 with at least 6.0 in each competency.

English language requirements:
Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Funding information:
Studentship type – UKRI through i-Risk Doctoral Focal Award. The studentship is for 3.5 years and provides a tax-free stipend of £21,805 per annum plus tuition fees at the UK rate. Excellent International candidates are eligible for a full international fee waiver however due to UKRI funding rules, no more than 30% of the studentships funded by this grant can be awarded to International candidates.

Bench fees required: No

Closing date of advert: 9th June 2026

Start date: October 2026

Full-time/part-time availability: Full-time 3.5 years, Part-time 7 years

Who is eligible to apply?: Both UK and International

How to apply:
All applications should be made online. Under programme name, select School of Science. Please quote the advert reference IRISK-26-LU-08 in your application. This PhD is being advertised competitively as part of the Informatics for Multi-hazard Risk and Resilience (i-Risk) NERC Doctoral Focal Awards (DFA) in the Environmental Sciences. Further details about i-Risk can be seen on their website https://github.com/i-risk-dfa.
Please note that your application will be assessed upon:
1. Motivation and Career Aspirations
2. Potential & Intellectual Excellence
3. Suitability for specific project
4. Fit to i-Risk
Please familiarise yourselves with i-Risk before applying. During the application process candidates will need to upload:

• A two-page personal statement split into two sections:
  • one page dedicated to your research interests in informatics and disaster risk reduction, the i-Risk DFA and your rationale for your choice of project
  • one page dedicated to answering the following questions: 1. Tell us about a time when you identified a new approach to a problem. What was your decision-making process? (~150 words); 2. Tell us about a time where you have performed data analytics. What was the task? What made it difficult? How did you handle it? (~150 words); 3. Tell us about a goal have you set for yourself that you have successfully achieved. How did you stay motivated? (~150 words); 4. Describe a situation where you demonstrated that you can constructively handle setbacks. How did you troubleshoot the problem? (~150 words)
• A curriculum vitae giving details of your academic record and stating your research interests
• Academic transcripts and degree certificates (translated if not in English)
• An IELTS/TOEFL certificate, if applicable

You are encouraged to contact potential supervisors by email to discuss project specific aspects of the proposed project prior to submitting your application. If you have any general questions please contact irisk@mailbox.lboro.ac.uk.
Please note that interviews are anticipated to be held remotely via Microsoft Teams week commencing 29 June 2026

Project search terms:
artificial intelligence, data analysis, data science, disaster, property risk, uncertainty, satellite, machine learning

Email Enquiries:
sci-pgr@lboro.ac.uk