AI for Sustainable Infrastructure and Climate Resilience

Summary

Research Context

Positioned within Ulster University’s School of Computing, this research theme applies artificial intelligence to environmental monitoring, climate resilience, food authentication and sustainable land management.

The projects focus on AI-driven wildfire impact assessment and decision support, imbalanced and noisy sensor data for food fraud detection and decision-aware environmental monitoring under uncertainty using Lough Neagh and its catchment as a real-world case study.

Research will combine multimodal remote sensing, satellite and UAV imagery, computer vision, sensor and spatial data, uncertainty estimation, anomaly detection and adaptive data collection.

Particular attention is given to efficient AI for remote and edge-computing environments and to systems that recognise when evidence is insufficient. The theme aims to support timely, evidence-based environmental and restoration decisions.

The projects address priorities in Agri-Tech, Low Carbon/Net Zero and Software/Cyber, supporting Programme for Government priorities around protecting Lough Neagh and the environment and growing a sustainable economy through AI-enabled environmental monitoring, climate resilience and evidence-based decision support.

AI-Driven Wildfire Impact Assessment and Decision Support Using Multimodal Remote Sensing and Computer Vision

Supervisor Names: Dr. Jorge Martinez Carracedo

This project will develop AI-driven methods for assessing wildfire impacts and supporting post-fire environmental management.

It will integrate satellite, UAV, ground-level imagery and environmental data using machine learning and computer vision to quantify fire severity and ecosystem response. Particular emphasis will be placed on efficient AI models suitable for edge-computing platforms, enabling rapid analysis in remote environments with limited connectivity.

The research aligns with Software/Cyber through advanced AI development, Low Carbon/Net Zero through improved climate resilience and ecosystem management, and Agri-Tech through technology-enabled monitoring of rural landscapes. Ultimately, the project will support evidence-based restoration and land-management decisions.

Decision-Aware Agentic AI for Adaptive Environmental Monitoring Under Uncertainty: A Lough Neagh Case Study

Supervisor Names: Dr. Naveed Ejaz

This PhD project will explore how AI can make better decisions when environmental data are incomplete, noisy or unreliable.

Using Lough Neagh and its catchment as a real-world case study, the research will develop an intelligent system that can recognise when there is not enough reliable information to make a prediction.

In such situations, the system could make a prediction, wait for better evidence, or identify what additional information should be collected, including what to measure, where and when.

The project will bring together machine learning, uncertainty estimation, anomaly detection and adaptive data collection, using sensor, spatial and satellite data.

A Data-Driven Multi-Measurement Gating Framework for Imbalanced Food Authentication  

Supervisor Names: Dr. Omar Nibouche

This project introduces an intelligent machine learning framework that combines crowdsourced, low-cost portable sensors with a data-driven multi-measurement gating network for grassroots food fraud detection.

By turning everyday citizens into the absolute first line of defence, this approach empowers ordinary shoppers to uncover hidden adulteration directly in fields, ports, and supermarkets.

By dynamically routing unreliable sensor inputs and utilising cost-sensitive learning, the framework overcomes class imbalance and noisy data to accurately isolate rare fraudulent anomalies in high-value commodities within massive streams of routine data.

Applying to Multiple Projects: Applications for more than one PhD studentship are welcome, however if you apply for more than one PhD project within Computing, your first application on the system will be deemed your first-choice preference and further applications will be ordered based on the sequential time of submission.

If you are successfully shortlisted, you will be interviewed only on your first-choice application and ranked accordingly. Those ranked highest will be offered a PhD studentship.

In the situation where you are ranked highly and your first-choice project is already allocated to someone who was ranked higher than you, you may be offered your 2nd or 3rd choice project depending on the availability of this project.

The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education.  We particularly welcome female applicants, as they are under represented within the school.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal detailing background, research questions, aims and methodology
• A comprehensive and articulate personal statement demonstrating interest in the subject area

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• International applicants should hold, or expect to hold, a First Class Honours Degree in a relevant subject area
• International applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to a distinction at Ulster
• Work experience relevant to the proposed project
• Track record of peer reviewed publications

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

PhD scholarships are available for this project to all applicants worldwide, regardless of residency or domicile, and will cover:

• Tuition fees.
• A maintenance allowance of approximately £22,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year, or part-time equivalent, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

Due consideration should be given to financing your studies.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.