Are you holding Master’s degree and looking for fully funded PhD positions? Queen’s University Belfast, Belfast, Northern Ireland invites online application for multiple funded PhD Programs / fully funded PhD positions in various research areas.
Candidates interested in fully funded PhD positions can check the details and may apply as soon as possible. Interested and eligible applicants may submit their online application for PhD programs via the University’s Online Application Portal.
1. Fully Funded PhD Position in Vegetation-wave interactions on mixing: transverse mixing of soluble pollutants around seagrass beds
Summary of PhD Program:
We are looking for a motivated candidate to lead a research project on pollution transport around coastal vegetation. The research project comprises an experimental study where pollution transport around vegetation is modelled in a large-scale experimental facility. A collection of measurement devices will be used for this study. The study aims to correlate water quality and quantity effects, identifying the contributions from different hydrodynamic conditions on mixing characteristics.
Application Deadline: 30 June 2025
2. Fully Funded PhD Position in Place and the Dynamics of Social Deprivation in Belfast 1991-2021
Summary of PhD Program:
The project will examine why some parts of Belfast have remained deprived since the 1990s when others have either become more or less deprived. It will tie into academic debates about the dynamics of social deprivation; for example, if areas change their deprivation profiles because of differential population inflows and outflows or because residents who remain in place become on average more (or less) deprived. Of particular interest is path dependency within neighbourhoods, the intergenerational transmission of social deprivation between parents and children, and how far neighbourhood is important in shaping life chances.
Application Deadline: 30 April 2025
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3. Fully Funded PhD Position in Migration, education, and development in the West of Northern Ireland
Summary of PhD Program:
The project will make use of the new linkage of the 2021 Census data to the Northern Ireland Longitudinal Study (NILS) to analyse migration (and staying) patterns and trends from 1991 as a context for the research but primary data collection via methods such as interviews, focus groups, and small-scale surveys will also be needed as appropriate.
Application Deadline: 30 April 2025
4. Fully Funded PhD Position in New advances in porous materials characterisation using low-field NMR
Summary of PhD Program:
This project aims to advance low magnetic field nuclear magnetic resonance (NMR) approaches for the study and characterisation of such functional porous materials, with a particular focus on NMR relaxation time measurements of confined liquids, which have emerged as an intriguing non-invasive approach for pore structure and/or interfacial characterisation efforts [1-4]. This methodology is expected to be particularly applicable to the study of confined oxygenated hydrocarbons, which are regularly employed as solvents and reagents in liquid-phase catalytic processes, including during the production of low-carbon fuels from waste.
Application Deadline: 30 April 2025
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5. Fully Funded PhD Position in Integrating CO2 capture and utilisation using dual functional materials and efficient induction heating
Summary of PhD Program:
This project aims to develop an integrated in-situ CCU solution coupling renewable hydrogen utilisation with CCU to produce syngas (CO and H2), which can be converted to liquid fuels and chemicals using commercial-ready technologies. The intensified CO2 capture and utilisation combine the regeneration of sorbent and the conversion of the captured CO2 into a single step, offering a much higher overall energy efficiency and potentially significant cost reduction compared to conventional CCU. Such a process is achieved by the development of multifunctional catalytic sorbents and the application of external fast-response heating to balance the thermodynamics of reactions for CO2 capture and CO2 utilisation.
Application Deadline: 30 April 2025
6. Fully Funded PhD Position in Cryogenic CO₂ Capture and Conversion from Biogas for Net Zero Fuels
Summary of PhD Program:
This proposal focuses on developing innovative cryogenic technology for efficient CO₂ capture from biogas, with an emphasis on converting captured carbon into value-added fuels. The research aims to enhance carbon utilisation, reduce greenhouse gas emissions, and contribute to achieving net-zero energy systems. Funding for this project is for 3 years and applicants should contact Dr Abul Quddusi (A.Quddusi@qub.ac.uk) for any queries related to the project.
Application Deadline: 30 April 2025
7. Fully Funded PhD Position in Enhancing community resilience and adaptive capacity through ocean literacy: an action research approach to community participation in marine planning
Summary of PhD Program:
The PhD Student will work alongside two Marine Social Science Researchers that are focusing on Ocean Literacy for their part in the TRACC project, and as Case Study Leads for Northern Ireland and Wales. The objective of this PhD is to develop a deeper understanding of adaptive capacity in the UK in relation to ocean literacy and to investigate how this informs and influences the adaptive capacity of coastal communities in the UK.
Application Deadline: 30 April 2025
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8. Fully Funded PhD Position in Advanced AI Methods for Analysing Nutritional Influences on Mental Health
Summary of PhD Program:
This project is a 4-year PhD project with enhanced training and 3+ month placement, which is funded by UKRI BBSRC, delivered by Queen’s University Belfast and Ulster University. Mental health conditions, such as depression and anxiety, affect millions globally, creating an urgent need for innovative strategies to improve care. While the impact of nutrition on brain function and mental well-being is increasingly recognized, uncovering how dietary patterns interact with genetic and pharmacological factors remains a challenge.
Application Deadline: 14 April 2025
9. Fully Funded PhD Position in Detection Learning for Detecting Virus Signatures
Summary of PhD Program:
This project is a 4-year PhD project with enhanced training and 3+ month placement, which is funded by UKRI BBSRC, delivered by Queen’s University Belfast and Ulster University. The emergence of novel pathogens poses significant global health challenges. Rapid and accurate detection of viruses and bacteria is essential for effective diagnostics and timely interventions. Machine learning approaches are commonly used; however, they often struggle with limited data scenarios and environmental variability.
Application Deadline: 14 April 2025
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10. Fully Funded PhD Position in Vector-AI: Knowledge-driven AI supports for vector borne disease analyses under climate change
Summary of PhD Program:
This project is a 4-year PhD project with enhanced training and 3+ month placement, which is funded by UKRI BBSRC, delivered by Queen’s University Belfast and Ulster University. Background: Vector-borne diseases are severe diseases caused by pathogens like parasites, viruses or bacteria that are transmitted to human and animals from infected hematophagous arthropods li ke mosquitos, ticks and fleas. These diseases, including Malaria, Dengue, Chagas disease, Yellow fever, or Japanese encephalitis, are responsible for 17% of all infectious diseases, leading to more than 700,000 annual deaths worldwide, more than any other causes combined. While others such as chikungunya, leishmaniasis and lymphatic filariasis cause chronic suffering, life-long morbidity, disability and occasional stigmatization.
Application Deadline: 14 April 2025
11. Fully Funded PhD Position in NILAB: Development of an AI-Integrated electrochemical-plasmonic biosensor for detecting AMR metabolic signatures
Summary of PhD Program:
This project is a 4-year PhD project with enhanced training and 3+ month placement, which is funded by UKRI BBSRC through the NI Landscape Partnership in AI for Bioscience (NILAB) Programme, delivered by Queen’s University Belfast and Ulster University. Details of the enhanced training will be available later at qub.ac.uk/nilab/. NILAB aims to bridge the gap between biology and artificial intelligence to accelerate bioscience discovery and foster effective collaboration between academia, industrial partners, and government bodies. NILAB’s mission is to train the next generation of researchers to develop and use AI to uncover the rules of life, addressing challenges in human health, animal welfare, and sustainable food systems.
Application Deadline: 14 April 2025
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12. Fully Funded PhD Position in NILAB: Diets to Optimise Individual and Planetary Health – A Machine Learning Approach (DIPH-ML)
Summary of PhD Program:
We aim to develop machine learning (ML) algorithms to identify dietary patterns that optimise individual and planetary health. Based on real-world food consumption data from the UK, we will assess environmental footprints of foods and dietary patterns. Using ML we will identify dietary patterns that facilitate ideal trade-offs between individual health (disease-free life expectancy, chronic disease burden, but also nutrient sufficiency) and environmental sustainability (indicators reflecting the planetary boundaries) under real-world conditions, for the first time using individual-level data from large-scale cohort studies.
Application Deadline: 14 April 2025
13. Fully Funded PhD Position in NILAB: Integrating AI and Taxonomy-Free Classification for Enhanced Microbiome Analysis of One Health Environments
Summary of PhD Program:
The student will be at the forefront of innovative research, working within the Creevey lab at Queen’s University Belfast (QUB) and collaborating with the Zheng lab at Ulster University (UU). The project involves linking pangenome profiles of available genomes to unique numerical identifiers from an existing database. The student will explore and implement various machine learning (ML) techniques, such as ensemble learning and graph-based approaches, to predict pangenomic profiles from these identifiers. Additionally, they will compare this new approach to existing methods and apply the models to real-world 16S rRNA data from diverse microbiomes, including agriculture, human, and wastewater environments.
Application Deadline: 14 April 2025
14. Fully Funded PhD Position in NILAB: EVOLVE-AI: Evolutionary-Informed Deep Learning for Deciphering and Modifying Cell-Cell Communication During Infection, Inflammation, and Cancer
Summary of PhD Program:
This project is a 4-year PhD project with enhanced training and 3+ month placement, which is funded by UKRI BBSRC through the NI Landscape Partnership in AI for Bioscience (NILAB) Programme, delivered by Queen’s University Belfast and Ulster University. Details of the enhanced training will be available later at qub.ac.uk/nilab/. NILAB aims to bridge the gap between biology and artificial intelligence to accelerate bioscience discovery and foster effective collaboration between academia, industrial partners, and government bodies. NILAB’s mission is to train the next generation of researchers to develop and use AI to uncover the rules of life, addressing challenges in human health, animal welfare, and sustainable food systems.
Application Deadline: 14 April 2025
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15. Fully Funded PhD Position in HAPPI: Harnessing AI for personalised nutrition to promote healthy ageing in older adults
Summary of PhD Program:
In the ‘HAPPI’ project, the successful PhD student will develop AI personalised nutrition solutions to improve nutritional status and functional abilities in older adults. The student will learn and apply state-of the-art AI techniques to existing large-scale datasets to create personalised nutrition solutions that can reverse and/or prevent undernutrition and improve health and wellbeing. The student will gain experience of working with diverse older adults in the community to design and test a feasible Mobile App to support long-term dietary behaviour change.
Application Deadline: 14 April 2025
16. Fully Funded PhD Position in HAPPI: Harnessing AI for personalised nutrition to promote healthy ageing in older adults
Summary of PhD Program:
The 4-year project provides an exciting opportunity to work with an Internationally recognized scientific team from Queen’s University Belfast to gain expertise in AI and translational public health nutrition and ageing research. There will be several training opportunities to develop knowledge and skills on nutrition and ageing, behaviour change, machine learning and Mobile App development as well as analytical data analysis skills. There is also the opportunity for a short-term placement with Age NI who will actively contribute to the project.
Application Deadline: 14 April 2025
17. Fully Funded PhD Position in AI-Driven Microbiome Profiling for Predicting Ocular Infection Severity
Summary of PhD Program:
As a PhD student, you will have the chance to work with cutting-edge bioinformatics software tools like KRAKEN2 and programming machine learning models such as neural networks and XGBoost. You will process genomic sequencing data, integrate clinical metadata, and validate your models with external datasets to ensure their clinical relevance and robustness. Your work will contribute to the development of an AI model capable of predicting infection severity based on microbial community profiles.
Application Deadline: 14 April 2025
18. Fully Funded PhD Position in AI-Driven Microbiome Profiling for Predicting Ocular Infection Severity
Summary of PhD Program:
This NILab project offers a unique opportunity for a fully funded PhD studentship in AI-powered bioscience, focused on improving the diagnosis and management of microbial keratitis—a leading cause of blindness. Our project will apply 16S rRNA sequencing and machine learning techniques to predict infection severity, addressing limitations in current diagnostic methods and ultimately improving patient outcomes.
Application Deadline: 14 April 2025
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19. Fully Funded PhD Position in AI-Driven Discovery and Characterization of Bacterial Secretion Systems and Their Effectors for Addressing Antimicrobial Resistance
Summary of PhD Program:
In this project the student will investigate, develop and validate novel bioinformatics and AI tools to study and identify BSSs. For this purpose, the project will consist in three main objectives. The first objective will consist in a critical review and benchmarking of the existing tools and methods. The second objective will focus on the development of novel bioinformatics solutions to identify BSS, BSS effectors and the immunity proteins necessary to prevent the toxic effects in the bacteria using them. As in other fields the quality and quantity of data used to develop and train any models is of paramount importance therefore this objective will include the generation of novel updated and extended datasets.
Application Deadline: 14 April 2025
20. Fully Funded PhD Position in AI-Driven Discovery and Characterization of Bacterial Secretion Systems and Their Effectors for Addressing Antimicrobial Resistance
Summary of PhD Program:
Bacteria have developed different ways to move proteins and other molecules across their membranes and in some cases across the membranes of eukaryotic cells. These movement depends on various secretion systems that are assembled as nanomachines and display multipurpose functions such as effective competition within an ecological niche by scavenging nutrients, or by injecting molecules (effectors) that kill surrounding bacteria (eliminating competence) and manipulate eukaryotic host cells (promoting inflammation, cell death and the establishment of a protected niche from the immune system).
Application Deadline: 14 April 2025
21. Fully Funded PhD Position in Using AI-driven computer vision and high-throughput multiplexed human cell infection assays to dissect intra- and intercellular signalling networks and discover new antimicrobials
Summary of PhD Program:
Infectious diseases caused by bacteria and viruses are a leading cause of mortality worldwide. The rise of antimicrobial resistance and a dwindling antibiotic development pipeline have prompted the WHO to declare a global emergency. Antibiotic development is hindered by our limited understanding of the complex processes at the host-pathogen interface. Traditional approaches are labour-intensive and low throughput, often investigating only a small set of proteins, one process, or pathway at a time.
