AI for Environmental Sustainability, Biodiversity and Climate Resilience

Summary

Positioned within Ulster University’s AI Research Centre (AIRC) in the School of Computing, this research theme focuses on advancing artificial intelligence for environmental sustainability, biodiversity monitoring, and climate resilience.

The work supports strategic priorities in Environmental Sciences, Software/Cyber.

PhD researchers will explore how AI-driven Earth observation, computer vision, and simulation technologies can address global challenges such as habitat loss, disaster recovery, and sustainable transport.

The projects aim to deliver explainable, trustworthy, and impactful AI solutions that enhance ecological monitoring, improve resilience planning, and promote sustainable resource management.

• Development of a Detection Transformer through Attentive Deep Learning and Explainable AI for Earth Observation and Result Visualisation

Supervisor Names: Prof. Yaxin Bi

The PhD researcher will investigate enhancements of deep learning’s predictive capabilities by integrating domain knowledge through attention mechanisms within transformer networks.

Traditional data-driven models struggle with convergence and generalisation due to limited contextual understanding.

Earth observation data will be studied in this research and aims to develop a novel transformer network with electromagnetic spectral analysis and satellite imagery, which will be used to monitor impact of environmental factors such as prediction of plant growth, water pollution, and environmental biodiversity loss.

The approach seeks to create robust, explainable models that reflect domain-specific insights, advancing deep learning’s applicability to achieve environmental sustainability.

• Post-disaster Damage Assessment Using Satellite Imagery and Machine Learning

Supervisor Names: Dr. Muhammad Shafi

This project will develop an AI-driven framework for rapid, automated post-disaster damage assessment in Northern Ireland.

In response to recent destructive storms, it will fuse satellite data (SAR, multispectral, and optical) with advanced deep learning to create accurate, region-specific damage maps. The research directly addresses the poor performance of global models in NI's unique mixed urban-rural context.

By creating a local damage atlas and fine-tuning models, the project will deliver actionable tools for emergency responders, planners, and insurers.

Expected outcomes include an open regional dataset and an operational web GIS dashboard to guide faster interventions and enhance regional climate resilience.

• Enhancing Driver Performance and Safety, Using Simulated Vehicle Data Analytics

Supervisor Names: Prof. Jonathan Wallace

This project develops a digital twin framework to analyse driver behaviour using simulated environments, eliminating the need for physical vehicles or roads.

By integrating telemetry and biometric data, it aims to optimise driving techniques—braking, cornering, gear use—to reduce fuel consumption and wear on tyres and brakes. Advanced analytics, including machine learning and process mining, will support high-impact research outputs.

The project aligns with strategic sustainability goals by promoting safer, more efficient driving practices in a risk-free, low-emission setting. It offers scalable insights for future transport innovations while contributing to environmental stewardship and road safety improvements.

https://youtu.be/Dzasl3mBiII?si=JgJIfK9S4Df4HPtU (1min video)

• AI-Driven Computer Vision for Automated Counting and Group Size Estimation of Scavenger Species in Camera-Trap Imagery

Supervisor Names: Dr. Jorge Martinez Carracedo

This project will develop AI-enabled camera traps to monitor scavenger species in Southern Spain, in collaboration with the Estación Biológica de Doñana.

Using large datasets of scavenger behaviour and population dynamics, it will also design and train machine learning models to estimate individual numbers and distinguish species in complex field conditions.

The resulting methods could later be applied to monitor waterfowl and scavengers in Lough Neagh, including key declining species such as the Eurasian curlew and marsh harrier, enhancing ecological knowledge and supporting sustainable management of the lake’s ecosystem and nutrient cycling processes.

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.

• Clearly defined research proposal detailing background, research questions, aims and methodology

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.

• First Class Honours (1st) Degree
• Masters at 70%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

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)

This scholarship will cover tuition fees and provide a maintenance allowance of £21,000* (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of approximately £900 per annum is also available.

To be eligible for these scholarships, applicants must meet the following criteria:

• Be a UK National, or

• Have settled status, or

• Have pre-settled status, or

• Have indefinite leave to remain or enter, or

• be an Irish National

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.

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) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.

*Part time PhD scholarships may be available, based on 0.5 of the full time rate, and will require a six year registration period