Ulster University, Ireland invites online Application for number of Funded PhD Opportunities in various Departments. We are providing a list of Fully Funded Doctoral Research Positions available at Ulster University, Ireland.
Eligible candidate may Apply as soon as possible.
(01) Funded PhD Opportunities
Summary/title: Active Lives, Healthier Futures: Understanding and overcoming barriers to physical activity among low-income families
This PhD provides an opportunity to address these pressing challenges through a programme of research with public involvement central throughout. The research will involve:
- A systematic review assessing the effectiveness of existing interventions targeting low-income families.
- A mixed-methods observational study to identify key factors influencing physical activity among disadvantaged families in NI.
- A survey and interviews with policymakers and service providers to understand barriers and opportunities for scalable solutions.
- The co-design and feasibility testing of an intervention developed in collaboration with Fermanagh & Omagh District Council, community partners, and families themselves.
Closing date : 27 February 2026
(02) Funded PhD Opportunities
Summary/title: Agentic AI for Financial Crime Detection: Adversarial Co-Evolution Methods
Financial crime detection systems struggle against adaptive criminals who evolve evasion tactics. Current compliance systems are reactive, detecting known patterns but remaining vulnerable to novel schemes.
This PhD develops autonomous AI agents that model the strategic co-evolution of financial crime schemes and detection systems, enabling proactive threat intelligence.
You will develop multi-agent systems where “fraudster agents” and “detector agents” engage in adversarial co-evolution through reinforcement learning.
Fraudster agents learn to evade detection; detector agents adapt to novel tactics.
This generates synthetic financial crime scenarios spanning money laundering, sanctions evasion, and mule networks, enabling stress-testing of compliance systems against threats that do not yet exist in operational data.
Working with Pytilia, a RegTech compliance technology provider, you will complete three annual placements (9 months total) gaining exposure to operational detection platforms and access to compliance experts for validation studies. Pytilia covers placement expenses and provides system access for stress-testing.
Closing date : 27 February 2026
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(03) Funded PhD Opportunities
Summary/title: AI approaches to investigation of the role of folate nutrition in breast cancer prevention: epidemiological, clinical, and molecular studies
Breast cancer (BC) is the most common cancer in women worldwide, with 2.3 million new cases and 670,000 deaths reported in 2022.
These figures represent 24% of all cancer diagnoses and 15% of cancer-related mortality among women (WHO 2022). Nutritional factors, particularly folate deficiency, have been increasingly recognised as contributors to cancer initiation and development (Pieroth 2018; Linhart 2009).
Folate (and synthetic folic acid) is an essential B vitamin central to one-carbon metabolism, a system of biochemical reactions that transfer single-carbon units required for DNA and RNA synthesis, amino acid interconversion, and cellular methylation pathways (Bailey et al. 2015).
Folate, together with vitamin B12, enables homocysteine remethylation to methionine, which is then converted into S-adenosylmethionine (SAM), the universal methyl donor.
Through SAM-dependent methylation, folate availability influences gene expression, tumour suppressor regulation, and proto-oncogene activity. Folate is also required for the thymidylate synthesis pathway (conversion of dUMP to dTMP), essential for accurate DNA replication and repair.
Closing date : 6 March 2026
(04) Funded PhD Opportunities
Summary/title: AI for Advanced Manufacturing
Positioned within Ulster University’s School of Computing, this research theme explores the integration of artificial intelligence with quantum technologies to advance next-generation manufacturing.
Specifically, this project explores the use of artificial intelligence to optimise the design of quantum computing hardware.
Specifically, it will involve the development of advanced optimisation and machine learning techniques for the design of superconducting qubits, one of the leading qubit modalities used in today’s quantum computers.
The optimal design of superconducting qubits is a highly complex, multi-objective optimization problem. It involves searching over materials, geometries and fabrication parameters to balance competing objectives such as coherence time, anharmonicity and scalability.
Recent experimental breakthroughs highlight the potential impact of making the correct design choices. For example, after a decade of only incremental improvement, a threefold increase in the coherence time of transmons was achieved by replacing the base metal niobium with tantalum.
Subsequent experimentally-driven research has investigated why this is the case, providing clues to further opportunities for better design.
AI-driven optimisation offers a promising parallel route forward. Techniques such as Bayesian optimisation have already proven successful in related contexts, such as optimising silicon quantum-dot entangling logic by tuning device parameters to balance gate speed and fidelity.
This project will develop Bayesian optimization and related approaches for the design of superconducting qubits.
Closing date : 27 February 2026
(05) Funded PhD Opportunities
Summary/title: AI for Environmental Sustainability, Biodiversity and Climate Resilience
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.
Closing date : 27 February 2026
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(06) Funded PhD Opportunities
Summary/title: AI for Sustainable Agri-Food Systems and Food Integrity
Positioned within Ulster University’s School of Computing, this research theme focuses on harnessing artificial intelligence and spectral technologies to strengthen food integrity and sustainability in agri-food systems.
The work aligns with sectoral priorities in Digital Health, Food Security, and Responsible Innovation.
PhD researchers will investigate cutting-edge approaches that combine machine learning with spectral data to enable rapid, non-destructive detection of food adulteration and fraud.
Machine learning combined with spectral data can play a vital role in combating food fraud by enabling precise and rapid identification of adulteration. Spectral techniques generate unique chemical fingerprints of food items, which machine learning algorithms analyse to detect inconsistencies and verify authenticity.
This project is an exciting opportunity to work on the integration of machine learning and spectral techniques to allow for real-time, non-destructive testing, reducing reliance on traditional laboratory methods and increasing detection efficiency. Overall, leveraging machine learning techniques for non-linear, sparse and small data sizes analysis to enhance food safety, protect consumers, and help prevent economic losses.
Closing date : 27 February 2026
(07) Funded PhD Opportunities
Summary/title: AI Storytelling in Screen Industries
There is both growing anxiety and optimism around the use of Artificial Intelligence (AI) in the creation of new content for film, television, and other screen media. Whilst there is increasing debate and literature on this subject, there remains limited scholarly and practical output—both in theory, practice, and policy.
This PhD project invites candidates to explore how AI is shaping storytelling techniques and creative practices within the screen industries in Northern Ireland, the wider UK, and beyond.
Applicants may adopt theoretical, practice-based, or mixed methodologies to investigate creativity, ethics, and the formal and technical potentials of AI-driven creative output.
Research Themes may include (but are not limited to):
* New storytelling techniques emerging from AI in screen media.
* Ethical and cultural challenges of AI-generated content.
* The impact of AI on creative labour, authorship, and industry practices.
* Technical innovations and their integration into film, television, and digital media production.
* Policy implications of AI for the creative industries.
Closing date : 27 February 2026
(08) Funded PhD Opportunities
Summary/title: AI-Augmented Rapid Determination of G-Quadruplex Structures from Sparse NMR Data
Transform how we determine biomolecular structures. This PhD develops generative AI tools that use minimal NMR data to rapidly predict 3D G-quadruplex DNA/RNA structures – a major leap beyond traditional, time-consuming methods.
Work at the intersection of generative AI, molecular dynamics simulations, and high-field NMR, using Ulster’s state-of-the-art facilities and Northern Ireland’s High-Performance Computing (NI-HPC) resources.
You will gain skills in:
* Generative AI for structure prediction
* Molecular dynamics simulations
* High-field NMR spectroscopy
Ideal for students excited by computational biophysics and structural biology, with strong career prospects in academia, biotech, and AI-driven drug discovery and diagnostics.
Closing date : 27 February 2026
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(09) Funded PhD Opportunities
Summary/title: AI-based Vision-Guided Robotic Machining: Intelligent Perception and Learning for Adaptive Manufacturing
Specifically, the project will have access to AMIC’s existing machining centres to conduct essential comparative trials, allowing the demonstrator’s performance to be rigorously benchmarked against traditional CNC machines on industrial test parts like aerospace ribs.
Key challenges this research will overcome include:
- Lack of Real-Time Monitoring: Current systems cannot accurately detect machining stress or predict tool wear/breakage in real time.
- Static Cutting Strategies: Few systems have the adaptive learning capability to optimise cutting parameters dynamically.
The research will focus on three core contributions:
- Multi-modal Vision Systems: Integrating advanced cameras (RGB, IR, high-speed) to provide real-time stress and tool monitoring.
- Adaptive Learning Framework: Employing Reinforcement Learning and adaptive control to predict tool failure and dynamically optimise toolpaths and cutting conditions.
- Explainable AI (XAI): Developing an operator interface with XAI to ensure trust and usability in the manufacturing cell.
Closing date : 27 February 2026
(10) Funded PhD Opportunities
Summary/title: AI-enabled Cross-Disciplinary Digital Twins for Built Environment
This proposal will cover the knowledge gap in developing integrated network of Digital Twins (inDTs) for cross-disciplinary Built Environment that can help assessing the performance of the core business of built assets (e.g. hospitals) in integration with the engineering systems to achieve Low Carbon and Net Zero targets.
This will create a new concept for developing inDTs which combines individual DTs for the core and non-core business process models, physical assets, and performance models to deliver advanced digital services.
As a particular case study example for the proposal, the work will focus on inDTs for the core processes of healthcare facilities (e.g. a hospital provides patients’ treatment services) and the non-core processes of these facilities to achieve Low Carbon and Net Zero targets (e.g. maintenance, equipment operation services, space management, etc.).
Closing date : 27 February 2026
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(11) Funded PhD Opportunities
Summary/title: AI-Enhanced Healthcare and Digital Transformation
The projects collectively address sectoral priorities in Life & Health Sciences and Software/Cyber, and are aligned with the Centre for Digital Healthcare Technology, part of the Belfast Regional City Deal.
Each PhD researcher will contribute to developing responsible, efficient, and trustworthy AI systems from cognitive fatigue management and personalised thermal comfort to multi modal medical imaging and IoT enabled healthcare.
The work aims to enhance patient safety, improve clinical decision making, and build public trust in emerging digital health technologies through secure, transparent, and ethical innovation.
- Passive Sensing and Adaptive Interventions for Managing Cognitive Fatigue in Everyday Contexts
Supervisor Names: Dr George Moore
Building on prior work detecting cognitive fatigue in mobile contexts, this opportunity addresses gaps highlighted in recent studies regarding low-burden, real-time support.
It investigates how passive sensing from smartphones and wearables can enable fatigue detection and trigger adaptive interventions. The methodology may involve interactive tasks simulating cognitively demanding contexts and collecting sensor data (Heart Rate Variability, touchscreen interactions, and mobility are collected).
Machine Learning models will be trained to infer fatigue in real time, triggering adaptive prompts, such as suggesting micro-breaks. Expected outcomes include a sensing pipeline, refined machine learning models, an intervention prototype, and insights into perceived user acceptability.
- Developing Efficient and Intelligent IoT-Enabled Healthcare Systems Using Small Language Models
Supervisor Names: Dr Tazar Hussain
This PhD project investigates how Small or Lightweight Language Models (SLMs ) can enhance Internet of Things (IoT) applications, with a focus on healthcare.
The research aims to develop efficient, adaptive, and privacy-preserving AI systems capable of real-time reasoning on resource-limited devices. Key challenges include computational and energy constraints, latency, data privacy, and model interpretability in safety-critical environments.
The project’s objectives are to design lightweight, edge-deployable model architectures, optimize their reasoning and adaptability, and evaluate their performance in healthcare IoT use cases, ultimately enabling cost-effective, secure, and intelligent digital health solutions that improve patient monitoring and decision-making.
- Multi-Modal Fusion Networks for Medical Imaging Analysis
Supervisor Names: Dr Shengli Wu
This PhD project develops trustworthy multi-modal fusion networks for medical imaging, integrating complementary data from MRI, PET, CT, and histopathology to enhance diagnostic accuracy and clinical reliability.
It addresses key challenges of data heterogeneity, missing modalities, and interpretability by designing transformer- and attention-based fusion architectures with robust and explainable mechanisms.
Through benchmark validation and clinician-informed evaluation, the research aims to produce transparent, robust, and responsible AI systems that support safe, interpretable, and equitable decision-making in healthcare, advancing trustworthy AI applications in radiology, oncology, and neurology.
- Personalised Thermal Comfort and Cardiovascular Health Model in Smart Homes Using Internet of Things and Deep Learning
Supervisor Names: Dr Matias Garcia-Constantino
The effects of global warming have intensified in recent decades, with 2024 recording the highest global temperatures ever.
Heatwaves, extreme temperatures, and droughts pose serious health threats, particularly by increasing cardiovascular risks-the leading cause of death in the UK. Vulnerable populations, such as the elderly and those with preexisting conditions, face heightened danger from temperature fluctuations.
This PhD project aims to develop an IoT-based personalised Thermal Comfort model for Smart Homes that integrates biofeedback and sensors with Deep Learning to dynamically optimise indoor conditions. The goal is to reduce cardiovascular risks by linking thermal comfort to cardiovascular stability.
Closing date : 27 February 2026
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(12) Funded PhD Opportunities
Summary/title: Alleviating hypoxia for enhanced cancer therapy
Whilst use of the above listed approaches provides enhanced therapeutic efficacy, we believe that a deeper understanding of the processes that underpin this enhancing effect at the cellular and molecular level will (1) maximize patient benefit, (2) inform more efficient clinical translation of this technology and (3) expedite commercial exploitation of this technology.
Specific objectives:
- To generate a range of tumour tissue samples from in vivo models treated with SGEN-33 and chosen partner therapies (PDT, SDT and radiotherapy).
- To establish a range of cell type identification and key signalling markers that can be used to identify cell type evolution/fate and signal process modifications post treatment.
- To leverage the data from ‘2’ above to monitor the effects of modifying therapy parameters with a view towards therapy optimization.
Closing date : 27 February 2026
(13) Funded PhD Opportunities
Summary/title: Amplifying Autistic Voices: A Co-Production Approach to Alternative and Augmentative Communication
Autism prevalence is rising. In Northern Ireland, the prevalence of autism among school-aged children is 5.9% (2024/25), representing a 42% increase from the 2019/20 rate of 4.2% (Information Analysis Directorate, 2025).
Delayed language development is one of the earliest indicators of autism, and 25–35% of autistic people remain minimally-verbal (Rose et al. 2016). These difficulties are linked to poorer educational, health, and social outcomes (Whitehouse et al. 2009).
Augmentative and Alternative Communication (AAC) supports autistic people to exercise their right to communicate (Communication Matters, 2023). AAC systems include mobile applications and speech-generating devices.
Effective AAC use depends on collaboration between professionals, families, and the user (RCSLT, 2024).
However, AAC tools are frequently developed without autistic involvement (Bolster and McCarthy, 2017). Co-production (i.e., working in equal partnership) is increasingly recognised as a neurodiversity-affirming approach to including people with autism in research (Nimbley et al. 2024).
Objectives:
- Identify autistic people’s communication priorities, challenges, and preferences.
- Co-produce a prototype AAC tool with autistic people.
- Evaluate the prototype’s accessibility and acceptability with autistic people.
- Test usability gathering feedback from autistic people.
Closing date : 27 February 2026
(14) Funded PhD Opportunities
Summary/title: An embedded investigation of a Whole Systems Approach to obesity in the Derry City and Strabane District
This PhD project will be embedded within this ongoing WSA initiative in the DCSDC area, working closely with DHC and a multi-agency leadership group, to generate critical evidence on the real-world implementation of Public Health England’s (PHE) six-phase WSA methodology.
The PhD project will focus on two core objectives:
1. To conduct an in-depth investigation into the barriers and facilitators affecting the implementation of the local WSA action plan within the DCSDC area.
2. To co-produce and pilot a novel monitoring and evaluation (M&E) framework designed to measure ‘system changes’ over time.
Closing date : 27 February 2026
(15) Funded PhD Opportunities
Summary/title: An exploration of psychosocial related quality of life and mental health literacy in Cystic Fibrosis (CF), and the impact of mental health outcomes.
Northern Ireland is estimated to have some of the highest prevalence per capita resulting in over 9,500 hospital admissions and 100,000 bed days per year in the UK.
Research has indicated that mental health concerns like depression and anxiety are common among people with CF (PWCF), emerging in childhood, and if untreated can have a negative impact on both physical and mental outcomes including lung function, pulmonary exacerbations, BMI and health related quality of life (Bathgate, Hjelm, Filigno, Smith & Georgiopoulous, 2022).
Symptoms of depression such as loss of appetite, fatigue, and insomnia, can overlap with CF related symptoms, ensuing further negative health implications and increasing burden on the NHS and its patients.
In CF, the prevalence of depression ranges from 29% among children and adolescents, 33% among adults, anxiety in adults has ranged from 30% to 33% (Smith, Modi, Quittner & Wood, 2010; Latchford & Duff, 2013).
Research has indicated that care pathways and provision of high-quality care for depression/anxiety should be in place prior to implementation of a screening programme (Quittner et al., 2016).
Closing date : 27 February 2026
(16) Funded PhD Opportunities
Summary/title: An exploratory study of green mobility strategies in Belfast, Northern Ireland, UK.
Belfast aspires to improve its residents’ quality of urban life, while becoming a more attractive city for businesses, and simultaneously being widely-known for its captivating amenities, heritage, and rich history.
According to the City’s 2023 Local Development Plan, Belfast is on a growth trajectory. However, Belfast has yet to fully resolve various urban issues typical of other cities in the UK of similar status, including rush hour traffic congestion, noise and barrier effects created by motorways, the demolition of some of the city’s built environment.
Belfast has been encouraging its residents to adopt more green mobility strategies, and especially the residents of newly built redevelopment housing complexes, to utilise the public transit system with affordable monthly passes, to cycle and walk the city’s increasingly connected bicycle infrastructure and improved sidewalks, respectively.
We welcome research proposals aimed at studying the city’s accessibility and mobility patterns, paying special attention to the City Council’s emphasis on green travel measures, as well as the Department for Infrastructure’s and Translink’s orientations to enhance and expand current transit services, foster transit-oriented development, and encourage single occupancy vehicle drivers to switch to greener modes of transport
Closing date : 27 February 2026
(17) Funded PhD Opportunities
Summary/title: An investigation of the genetic causes of medication side effects in the UK
In the UK and Ireland, an aging population are increasingly living with multiple long-term health conditions (MLTCs). Along with increased prevalence of depression in young people and people of working age, those with MLTCs have added considerable pressure on GP practices and community pharmacies, and increased waiting times for patients.
Adverse drug events (ADEs) frequently occur in those with multiple long-term health conditions who are taking multiple medications and are thought to account for 1 in 6 hospital admissions in the UK.
Common variations in our genes can impact how we break down prescribed medications in our bodies (aka pharmacogenes) and this can be a key factor in how much benefit we get from treatments.
Aims
This PhD project will address knowledge gaps in:
- Pharmacist and public perceptions and understanding of pharmacogenomics in Northern IreIand (NI).
- The incidence of pharmacogene-driven medication side effects and ADEs in NI and UK populations.
- Longitudinal analysis of PGx prescribing’s impact on ADEs and quality of life in NI.
Closing date : 27 February 2026
(18) Funded PhD Opportunities
Summary/title: Applications of Artificial Intelligence (AI) in Fire Safety Design
Accidental fires continue to pose significant threats to public safety and building resilience and have far-reaching consequences—physical, psychological, economic, social, and environmental.
In response to the tragical Grenfell Tower fire in June 2017, the UK Government enacted the Building Safety Act 2022, establishing the Building Safety Regulator (BSR) and introducing stringent safety checks for higher-risk buildings throughout their lifecycle.
Despite these efforts, there are still significant challenges to providing overall fire safety in buildings, especially in the early stages of building design and the incorporation of effective evacuation strategies.
Current regulations often fail to address emerging risks associated with innovative materials, advanced systems, and modern construction technologies.
Large full-scale compartment fire tests, while providing the best insight into fire development, are extremely expensive and often take months if not years to conduct.
Instead, computational fluid dynamics (CFD) models, are used to aid fire safety design, but they are often computationally intensive and time-consuming, hindering reliable fire safety assessments.
Artificial intelligence (AI) models offer innovative solutions to these challenges in fire safety design, as they can rapidly analyse complex systems, identify patterns, and predict fire behaviour with high accuracy, enhancing decision-making and resource allocation.
Closing date : 27 February 2026
(19) Funded PhD Opportunities
Summary/title: Assessing Effect of Greenhouse Gases Emission Reduction by Satellite and earth observations with the impact of implementation of Variable Renewable Energies in Marine Climate Islands
The UK has set a Net Zero target by 2050, which means no longer adding to the total amount of greenhouse gases in the atmosphere.
The two main greenhouse gases (GHGs) are carbon dioxide (CO2) and methane (CH4). Integrating renewable energy for energy supply will be a key solution, especially to find an optimized solution with renewable energy (RE) implementation for power generation to replace fossil fuels.
The effects of reducing greenhouse gases through the implementation of renewable energy sources are complicated and influenced by multiple factors, including geographical area, availability and intermittency of renewable energy sources, peatland CO2 release, local economy, and policies, etc.
It is essential to study the reduction of CO2 & CH4 with different renewable energy installations as a comprehensive task and also worth exploring the effects of individual factors.
Satellites provide the ability to retrieve XCO2, and their XCO2 data products have been used to improve our knowledge of natural and anthropogenic CO2 & CH4 sources and sinks.
Closing date : 27 February 2026
(20) Funded PhD Opportunities
Summary/title: Bio-Inspired Through-Thickness Reinforced Composites
Carbon fibre reinforced polymer composites, CFRP, offer superior strength-to-weight ratios when compared with materials traditionally used in the aerospace and automotive industries.
However, structural discontinuities, such as fastener holes, can introduce stress concentrations in these anisotropic material systems increasing the risk of critical failure due to delamination.
However, delamination of composite structures is a considerable drawback that can be potentially solved by a through-thickness reinforcement, TTR, of the carbon fibre fabrics prior to the resin impregnation step.
While a number of these TTR methods exist; tufting, z-pinning, stitching, 3D weaving etc, they are often examined in isolation with “off the shelf” reinforcement materials.
This project investigates bio-inspired through-thickness reinforcement techniques, specifically tufting, 3D weaving and z-pinning, to enhance delamination resistance in laminated CFRP structures.
Tufting and z-pinning both introduce a material through-the-thickness of the laminate which act as mechanical pins arresting the displacement caused by crack opening displacement in mode I loading and crack sliding in mode II.
Tufting provides reinforcement in both the site of the insertion of the tufting yarn and between the tufts, whereas z-pinning does this at the site of insertion. 3D wovens involve layering and interweaving fibres in a computer-controlled process, weaving through-thickness reinforcements directly into a component.
Closing date : 27 February 2026
(21) Funded PhD Opportunities
Summary/title: Can community development approaches in social work deliver sustainable cost effective solutions to improving community health and well being? An investigation into the best practice models to transform health and social care in N I
This research investigates the role of community development as a multi-disciplinary approach to reduce fragmentation, enhance strategic alignment and productivity across LHS practice (Theme 1). Findings will inform policy, training and practice in health & social care and allied professions, to enhance the quality of work and retention of talent (Theme 2: Action 8).
The Programme for Government (2024-2027) highlights the huge pressure on health services in NI, with increasing demands predicted by 2043, and a significant rise in the proportion of older people (85 years+).
To reform and transform public services, there is an urgent impetus to forge more joined-up approaches across HSC sectors, as well as with the CVS. MacDonald et al.’s (2024) systematic review across the UK and internationally, supports the effectiveness of CDA in tackling health inequalities.
Whilst the DoH has an extensive history of policies and publications promoting CDA in SW (see Pascoe et al., 2025), including their assertion that a healthy community is “…more self-reliant and is less likely to place increased demands on the health and social care system” (DoH, 2018a, p.44), Proctor (2017) highlights the lack of evidence supporting the effectiveness of different SW interventions.
As a front-line service, SW interventions have a huge impact on people’s lives and given the current and predicted demands on HSC services, robust evidence is required to determine the impacts of CDA on health and wellbeing. Whilst O’Brien (2023) has produced promising evidence pertaining to the role of CDA in SW, the evidence is largely limited to social workers’ perceptions.
Closing date : 27 February 2026
(22) Funded PhD Opportunities
Summary/title: Causal and Dynamic Modelling of Milk Production in Grass-Based Dairy Production System: Integrating Dietary, Environmental, and Cow–Calf Production Dynamic
The research will address key questions including:
RQ1: What are the key causal drivers and dynamic feedback loops influencing milk production in grass-based dairy production system?
RQ2: How do time-varying dietary factors (feed quality, feed intake, energy density) interact with environmental and physiological variables to influence milk production over time?
RQ3: How does the cow–calf production dynamic including calf birth traits and early growth impact maternal milk production?
RQ4: How can causal AI and dynamic modelling be integrated to improve prediction accuracy of milk production while maintaining interpretability?
RQ5: How can counterfactual simulations support decision-making in optimising feeding and nutrition strategies and improving dairy herd management and health?
Closing date : 6 March 2026
(23) Funded PhD Opportunities
Summary/title: Children’s Language in Northern Ireland: a comprehensive profile and needs analysis
This project aims to develop a comprehensive language profile for children in Northern Ireland: how children’s language varies throughout primary school, which factors predict variation in children’s language, and how these factors are distributed throughout Northern Ireland.
This profile will provide an evidence base for a needs analysis to support children’s language skills, particularly those which support engagement with core subject areas. The needs analysis will inform recommendations for practitioners and policy-makers to foster environments that support language development across different backgrounds, from the very start of and throughout primary school.
Closing date : 27 February 2026
(24) Funded PhD Opportunities
Summary/title: Clinical and Molecular Investigation of GCPR Pathway Modulation by Omega-3 Fatty Acids in Insulin Resistance, Prediabetes and Type 2 Diabetes
The studentship aims to investigate:
- The therapeutic effects of naturally occurring omega-3 polyunsaturated fatty acids (PUFAs) that selectively target G protein-coupled receptors (GPCRs), focusing on their role in modulating metabolic and inflammatory pathways in early-onset diabetic models and GPCR-knockout models
- Gene knockout of GPCRs using CRISP-R gene editing, fluorescence microscopy, mass spectrometry, PCR and western blotting to investigate the common receptor-mediated downstream signalling molecules activated in diabetes and cardiovascular disease.
- The anti-diabetic and anti-inflammatory effects of GPCR activation by naturally occurring omega-3 polyunsaturated fatty acids in clinical studies in patients with obesity, prediabetes and type 2 diabetes
Closing date : 27 February 2026
(25) Funded PhD Opportunities
Summary/title: Co-design and evaluation of an assessment and communication framework to support health and social care professionals in managing suicide ideation for patients with head and neck cancer
Aim
To co-design and evaluate an intervention that supports HSCPs in assessing and communicating with HNC patients experiencing suicidal ideation.
Objectives
*Identify and evaluate existing interventions for suicide ideation assessment in cancer care.
*Explore the needs of HNC patients and caregivers.
*Co-design a tailored assessment and communication framework.
*Test feasibility and acceptability through user engagement.
*Evaluate effectiveness via a pilot randomised controlled trial (RCT).
Closing date : 27 February 2026
(26) Funded PhD Opportunities
Summary/title: Co-designing local solutions to poverty with experts by experience
This is a Collaborative project with the Northern Ireland Anti Poverty Network (NIAPN). NIAPN will actively support the PhD researcher by helping to identify networks to engage and recruit potential participants, provide training in participatory research methods, provide mentorship to enhance policy and professional skills, build network opportunities, and provide training and mentorship on an ethical approach to working with people in poverty.
Efforts to tackle poverty in NI have been limited. The NI Executive made a legal commitment to adopt a strategy to tackle poverty, social exclusion and patterns of deprivation based on objective need.
This legal commitment is enshrined in Section 28E of the NI Act 1998. To date, the Executive has failed to deliver this legal duty. In 2015 and 2025, the Committee on the Administration of Justice (CAJ) took a judicial review against the NI Executive for the failure to implement an anti-poverty strategy based on objective need.
Closing date : 27 February 2026
(27) Funded PhD Opportunities
Summary/title: Comprehensive Innovation for Sustainable Development
Sustainable development, emerging artificial intelligence systems and the need for different skills competencies require a more inclusive approach to innovation.
This inclusive approach has been termed Comprehensive Innovation by the United Nations and has been identified as a key area of focus for the Northern Ireland innovation ecosystem.
Three research areas have been identified within the remit of Comprehensive Innovation that are of particular importance for the Northern Ireland economy:
*Engagement with international trends in terms of economic and social development that is human centric, sustainable and resilient.
*The development of future skills that demonstrate agility, interrelationships, and systems perspectives.
*Alignment with international innovation management standards.
Closing date : 27 February 2026
About Ulster University, Ireland – Official Website
Ulster University (Irish: Ollscoil Uladh;Ulster Scots: Ulstèr Universitie or Ulstèr Varsitie), legally the University of Ulster, is a multi-campus public research university located in Northern Ireland. It is often referred to informally and unofficially as Ulster, or by the abbreviation UU. It is the largest university in Northern Ireland and the second-largest university on the island of Ireland, after the federal National University of Ireland.
Established in 1865 as Magee College, the college took its modern form in 1984 after the merger of the New University of Ulster established in 1968, and Ulster Polytechnic, incorporating its four Northern Irish campuses under the University of Ulster banner. The university incorporated its four campuses in 1984; located in Belfast, Coleraine, Derry (Magee College), and Jordanstown. The university has branch campuses in both London and Birmingham, and an extensive distance learning provision. The university rebranded as Ulster University in October 2014, including a revised visual identity, though its legal name remained unchanged.
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