You will earn 6 research credits over 8 weeks, conducting faculty-supervised, hands-on, directed study research projects with results that will culminate in the preparation of a research paper. You will complete a minimum of 240 hours on research in and out of the laboratory.
Faculty mentors will work closely with you to direct your continued growth and knowledge development in the chosen research topic discipline.
| Course ID | Title | Credits | Syllabus |
|---|---|---|---|
| LIME RSLW 392S | International Independent Research in STEM Fields | 6 |
With the rise of high-density computing due to advances in AI, cryptocurrency, etc. there is an ever-greater need for efficient cooling of data centres. Traditionally, air-cooling was the medium of choice for this. However, as power dissipation levels increase dramatically, air cooling can be less effective. An alternative is to use immersion cooling, where the electrical components are immersed in a dielectric fluid to absorb the heat. This heat is then rejected via a heat exchanger. Immersion cooling offers many obvious benefits, but it also poses challenges - one of which is the choice of dielectric fluid. This project seeks to review the range of fluids currently being used in commercial applications, to characterise their performance and identify KPIs, and ultimately propose some novel engineered fluids based on this characterisation exercise. This is largely a theoretical project, with fluidic characterisation undertaken using MATLAB in conjunction with REFPROP/Aspen/ThermoData Engine. Project is suitable for someone with strengths and interests in fluids, thermodynamics, and heat transfer.
Relevant Majors: Mechanical Engineering
This project aims to evaluate a new class of lightweight LLM architectures that are energy-aware, and integrate sustainability considerations at the architectural design stage for scalable, multi-platform deployment. Recent advances in large language models (LLMs) have revolutionised AI applications. However, despite their utility, the environmental cost of training and deploying such models is substantial. In addition, smaller, “lightweight” models consume significant energy during inference, especially when deployed on edge or low-resource devices. Understanding and reducing this energy consumption is essential for developing sustainable and scalable AI systems. Current literature often focuses on model accuracy or latency but gives limited attention to energy-per-token or carbon-per-query metrics, which are essential for evaluating sustainable AI deployment. This project addresses this by evaluating the energy performance of lightweight LLM architectures across different inference configurations and hardware environments, identifying strategies for energy-aware AI operation.
Relevant Majors: Software Engineering, Computer Science
The aim of the project is to develop a taxonomy and prioritisation framework for the Green Software Foundation Green patterns that supports their systematic classification and strategic adoption. While these patterns provide valuable guidance for reducing the environmental impact of software systems, they currently lack an integrated taxonomy or framework that enables practitioners to assess their relative impact, feasibility, and applicability across different software contexts. This limits their adoption and the ability of developers, educators, and organisations to make evidence-based decisions about which patterns to implement first for maximum sustainability benefit. The project’s objectives are to: 1. Analyse existing GSF patterns to identify shared attributes, dependencies, and contextual factors. 2. Develop a structured taxonomy that classifies patterns according to software lifecycle stages, impact domains, and system layers. 3. Design a prioritisation model that ranks patterns based on their potential environmental impact and implementation feasibility. 4. Produce a visual and descriptive framework that communicates these relationships clearly for use by researchers, practitioners, and educators. As a result, the project aims to make the GSF patterns catalogue more actionable, enabling the community to focus on the interventions most likely to deliver measurable sustainability gains.
Relevant Majors: Software Engineering, Computer Science
The aim of this project is to develop key performance indicator (KPI) artifacts that can be used to estimate the impact on each sustainability dimension within the Sustainability Awareness Framework (SusAF). The artifact will be developed using Design Science Research (DSR) methodology. The project requirements are defined, and the need for KPIs is identified in the relevance cycle. To ensure that the artifact is both innovative and built on established knowledge and practices, the rigor cycle draws upon the relevant knowledge base. This includes reviews of methods for developing and validating KPIs, as well as relevant literature. The artifact is created and refined in the design cycle informed by input from both the relevance and rigor cycles. However, DSR is not a linear process; rather it is iterative, involving continuous interaction between designing and evaluating the artifact in response to insights from the relevance and rigor cycles. In this thesis, the evaluation of the KPIs could be done in a workshop.
Relevant Majors: Software Engineering, Computer Science
The aim of this project is to design and validate an integrated sustainability pipeline that links Sustainable Organization Framework for Technology Development (SOFT), Sustainability Awareness Framework (SusAF), and Sustainability Assessment Framework (SAF) enabling consistent prioritisation and traceable design decisions for green software initiatives. Despite the growing number of sustainability frameworks in software engineering, there is currently no unified process that connects high-level sustainability strategy with software design and architectural decision-making. Frameworks such as SOFT (Sustainable Software Framework), SusAF (Sustainability Awareness Framework), and SAF (Sustainability Assessment Framework Toolkit) each address different layers of sustainability from governance and awareness to measurement and implementation. However, they operate largely in isolation, making it difficult for organisations and software teams to translate sustainability intent into measurable design actions and evidence-based improvement. This fragmentation results in gaps between awareness, assessment, and architectural practice, limiting the consistency, comparability, and traceability of sustainable software initiatives. The project addresses this by developing a sustainability pipeline that combines SOFT, SusAF, and SAF into a cohesive workflow. The pipeline will guide teams from strategic sustainability goals (SOFT) through awareness and scoping (SusAF) to structured evaluation and architectural modelling (SAF), ensuring that sustainability considerations are embedded, measurable, and traceable throughout the software lifecycle.
Relevant Majors: Software Engineering, Computer Science
The waviness of our hair is affected by how it grows (genetics, nutrition etc), how we style it – and even gravity! When it comes to styling, heat, moisture and how gentle or rough we are all play a role. In this project you will use mathematical modelling to explore the factors that change the waviness of our hair. This will involve developing and analysing the differential equations that describe the physics and mechanics of hair curliness.
Relevant Majors: Mathematics & Statistics
Large Language Models (LLMs) have emerged as the current state-of-the-art for a broad spectrum of AI tasks. This project focuses on the problem of fine-tuning pretrained LLMs, which involves using pretrained weights as initialization to improve convergence and reduce training time. However, due to the immense number of trainable parameters involved, the fine-tuning process of such models is often computationally expensive and resource-intensive. Low-Rank Adaptation (LoRA) is a recent method for fine-tuning LLMs that substantially reduces the number of learnable parameters. Numerous recent studies have explored LoRA-based approaches to minimize trainable parameters while improving training efficiency and performance. This project investigates the trade-off between model performance and the number of trainable parameters when applying LoRA-based fine-tuning to pretrained LLMs.
Relevant Majors: Software Engineering, Computer Science
This research investigates the optimisation of microfeature resolution in clear resin 3D printing for advanced engineering applications. Targeting feature sizes as small as 50 microns, the study employs a Design of Experiments (DoE) methodology to identify optimal process parameters that enhance print fidelity, transparency, and structural integrity. The project aims to enable reliable fabrication of micro-scale components for applications such as microfluidics, optofluidics, biomedical devices, and MEMS prototyping. Outcomes will contribute to improved design guidelines and process control strategies for high-resolution additive manufacturing using photopolymer resins.
Relevant Majors: Mechanical Engineering, Materials Science
Digital Twin technology allows physical systems in manufacturing to be mirrored in a virtual environment for real-time monitoring, prediction, and optimisation. This project will introduce students to the development of Digital Twins using sensor data and networked devices. Students will gain experience in linking hardware (IoT sensors) with software models and exploring applications in predictive maintenance and production efficiency. By the end of the 8 weeks, students will have created a prototype Digital Twin demonstrating data collection, visualisation, and analysis in a manufacturing context.
Relevant Majors: Computer Engineering
A wide variety of bacteria use the microbial communication system called quorum sensing (QS), which allows them to modify their behaviour collectively in response to changes in cell density. This communication is mediated by small molecules accumulated during microbial multiplication and involves the production, secretion, and detection of extracellular signalling molecules, known as autoinducers. The communication mediated by QS can be interrupted in several ways: by inhibiting the autoinducer synthesis, through enzymatic degradation of autoinducers, or by competition for binding to receptor proteins, ultimately inhibiting the target gene expression, mediated by interfering molecules called quorum sensing inhibitors (QSI). This study aims to evaluate the ability of some artificial and natural non-nutritive sweeteners (NNS) ( in combinations found in the food market) to inhibit QS and its possible implications for the gut microbiome.
This project focuses on analyzing data from simulated racing games to discover which features best explain player performance. By combining eye-tracking information (where players look during the race) with telemetry data (speed, braking, steering, lap times, etc.), students will use feature-selection methods to identify the most important factors that separate skilled drivers from beginners. The goal is to highlight which data points give the strongest insights into player behavior and success, which could help improve training tools, driver feedback systems, or competitive performance analysis.
Relevant Majors: Data Analysis, Software Engineering, Computer Science
Cancer cell profiling helps identify specific molecular features and vulnerabilities in tumours. By studying how breast cancer cells respond to new drugs, researchers can discover biomarkers that indicate how well a treatment will work or identify tumours that are more likely to be sensitive to these drugs, ultimately improving the chances of treatment success. Transcriptomics (measuring RNA transcripts) is a powerful tool for evaluating the effects of new drugs on gene expression in cells. Next-generation sequencing (NGS) enables us to sequence and quantify the mRNA transcripts of thousands of genes simultaneously, providing a comprehensive snapshot of gene expression patterns in response to drug treatment. Using NGS transcriptomics we will explore gene expression in breast cancer cells treated with a new anti-cancer epigenetic drug, developed in Dr Brown's group. This profiling reveals how the drug kills breast cancer cells, and helps identify patients where this drug will be most effective.
Sheep wool is a sustainable, organic and renewable material which can be used in a wide range of applications, including building construction. Although largely underused in the present commercial situation, it has huge potential as a natural material in Ireland's growing bioeconomy. Students conduct extensive research into the production of wool in Ireland, its current and potential uses in construction and beyond, liaising with experts in the field and drawing on a broad range of available resources. Students collate the information gathered and produce a comprehensive design guide for the use of sheep wool in building construction and/or product design, including detailed drawings and technical specifications. This research may be complemented by material experiments.
Relevant Majors: Materials Science, Architecture, Design
Endometriosis (EM) is a chronic inflammatory, gynecological condition characterised by the presence of endometrial tissue-like lesions in areas outside the uterine cavity. EM affects 10% of women of reproductive age worldwide. The main symptoms include infertility and chronic pelvic pain. Currently there is no cure for endometriosis with management options limited to surgical removal of ectopic tissue, hormonal treatment to suppress and delay recurrence and progression of disease and pain suppression strategies. Recent evidence points to a pathogenic role for the NF-κB family of inducible transcription factors in EM where NF-κB drives endometriotic cell survival, resistance to apoptosis, proliferation, invasion and adhesion. This project focuses on the experimental validation of transcriptomic data obtained from RNA sequencing (RNA-seq) of an NF-κB-deficient endometriotic cell model. The primary objective is to confirm differential gene expression patterns identified through RNA-Seq using quantitative real-time PCR (qRT-PCR). Selected genes of interest - based on statistical significance, biological relevance, and pathway enrichment - will be assessed to ensure reproducibility and accuracy of the sequencing data.
Taking a look at the reference section of many articles published in computer science today, there is an increasing tendency to include arXiv articles. This allows the author to refer to more recent work but the downside is that the articles have not undergone peer review. This project will quantify the issue for top computer science/software engineering journals/conferences and will aim to assess the impact, through a qualitative diff/impact analysis of the peer-review process, by comparing the arXiv articles to their subsequently published articles (if the articles are subsequently published).
Relevant Majors: Computer Science, Software Engineering
Concussion is the most common form of mild traumatic brain injury and can cause long-lasting effects. Although the exact mechanisms are unknown, concussion is recognized as a subset of neurological injuries involving complex pathophysiological processes. There are many different causes of concussion, and not all of them involve direct impact. Non-impact situations, such as whiplash—one of the most common injuries following a car crash—can also cause concussions.
Developing more accurate models of concussive injuries will help researchers identify key markers post-injury, improving diagnostic processes and prevention methods. This project aims to develop a mechanical concussion rig to apply a non-impact rotational acceleration injury to a model skull, accurately mimicking concussion-inducing scenarios. It will explore current methods for studying concussions and address the limitations of previous iterations of this model developed at UL.
Relevant Majors: Biomedical Engineering, Mechanical Engineering
Machine learning models, especially neural networks, pervade modern society, and are now making decisions in a wide range of scenarios such as medical diagnosis, fraud detection, self-driving cars, and industrial process modelling, among many others. However, there is growing concern that these models lack statistical development. Consequently, they tend to be overly complex and may overfit data. On the other hand, while balancing data fit and model complexity is a core aspect of statistical modelling, neural networks have not historically been developed from a statistical perspective. Therefore, the aim of this project is to take a statistical modelling approach to neural networks by applying a stepwise information-criteria-based input- and hidden-neuron selection procedure. Using both real and simulated data, the neural networks selected using this procedure will be compared to more complex networks that lack selection.
Relevant Majors: Mathematics, Computer Engineering
Classical data analysis involves data stored in a table, where each row represents an individual observation and each column is a numeric variable measured for that individual. While tabular data has been the focus of traditional statistical modelling for hundreds of years, in the modern world, we often encounter other data modes beyond tabular data, for example, free text, images, sound, video etc. However, machine learning methods (particularly deep learning) have been quite successful in handling multi-modal data. The aim of this project is to explore multi-modal machine learning models, assess the impact of considering the different data modalities, and compare with more traditional statistical methods.
Relevant Majors: Mathematics, Computer Science
Opinion dynamics models are mathematical models to describe how people’s opinions change over time: two people can discuss a topic together, after which their views may converge to be closer, or could perhaps move further apart. Another factor that impacts the dynamics of how groups of opinions form is the social connectivity of people, e.g., through physical or online social networks, which also change over time. Two popular models for understanding social dynamics are the “Axlerod” and “Deffuant” model, both of which have some similarities, but also important differences in the way they assume opinions to spread and change. This project aims to compare these models numerically by setting up synthetic populations of individuals to which the models will be applied. We will also explore their application on real data (e.g., surveys and social media feeds) and the potential for combining some aspects of the model for more realistic opinion spread.
This project investigates how different interpolation techniques influence the prediction of mechanical properties in heat-treated maraging steel 300 produced by Laser Powder Bed Fusion (L-PBF). Using an existing Python tool [https://github.com/singhad/plastic_anisotropy_pipeline], the study will compare six interpolation methods—Akima, Radial Basis Function, Kriging, Linear, Nearest, and Biharmonic—to assess their impact on key outputs such as yield strength, strain anisotropy (R-values), ductility, etc. The student will implement a configurable workflow to apply each interpolator, quantify sensitivity through error metrics and parameter drift, and develop guidelines for method selection. This work addresses the critical role of data interpolation in ensuring accurate stress and strain anisotropy characterization for additively manufactured metals, where sparse or noisy experimental data can significantly affect predictions. This is primarily a computational project, where existing experimental data will be used, but it may also involve experimental work to collect more data necessary for the purpose of this project (via mechanical testing).
Relevant Majors: Materials Engineering
Urea (NH₂CONH₂) is a vital nitrogen fertilizer and chemical feedstock, traditionally produced through energy-intensive Haber–Bosch ammonia synthesis followed by CO₂ utilization. This process consumes nearly 2% of global energy and contributes substantial CO₂ emissions. Direct electrochemical co-reduction of CO₂ and NOₓ to urea under ambient conditions offers a sustainable, integrated pathway to couple carbon and nitrogen cycles using renewable electricity. However, major challenges include activating stable CO₂ and NOₓ molecules, controlling C–N coupling intermediates, and suppressing side reactions such as hydrogen and ammonia formation.
This project targets the design of efficient, selective electrocatalysts for urea synthesis. Focus will be placed on 3d transition metal phosphides (TMPs) for their tunable electronic properties and dual-atom catalysts (DACs) for cooperative adsorption and C–N coupling. Enzyme-inspired catalysts modeled on carbon monoxide dehydrogenase and nitrate reductase will also be explored.
A combined computational–experimental framework using Grand Canonical Density Functional Theory (GC-DFT) and electrochemical testing will guide catalyst optimization. Expected outcomes include stable, high-selectivity catalysts and mechanistic insights into C–N bond formation, enabling sustainable urea production from CO₂ and NOₓ toward a circular carbon–nitrogen economy. The catalysts will then be synthesized in the framework of the EU-project SUN2CN (2025-2029).
Relevant Majors: Physics, Chemical Engineering
Plant proteins represent the primary alternatives to animal-derived proteins; however, they are often limited by suboptimal nutritional, techno-functional, and sensory properties. This project aims to address these challenges by employing chemical approaches to modify the structure of various plant proteins, including pea and rice proteins, with the objective of developing nutritionally enhanced beverages. The work will involve protein hydrolysis, chemical modification, and in vitro digestion studies to assess the resulting structural and functional changes. Ultimately, a prototype beverage will be formulated, and its physicochemical and sensory properties will be characterized and compared with those of commercially available plant protein-based products.
Today we face huge challenges regarding energy production and storage with many countries still heavily reliant on fossil fuels. Materials that have enhanced energy storage and conversion capabilities are being researched as alternatives to fossil fuels. One family of these materials is alkali metal-based chalcogenides which can be produced in the nanometer size range. By taking advantage of the size dependent energy conversion capabilities and using earth abundant and benign elements to make these nanocrystals, it is possible to develop a more sustainable solution to help meet the demands of the global energy shortage.
These materials are produced by colloidal synthesis, a Nobel Prize winning method. The student will carry out colloidal synthesis experiments looking at the impacts of elemental precursors and ligands on the reaction. They will form the nanocrystals and investigate how their structure and composition serves to impact the next generation of energy storage and conversion technologies. The student will gain experience using a variety of techniques (XRD, SEM, TEM, FTIR) to characterise the materials. This project will allow the student to learn about inorganic colloidal chemistry and apply their knowledge on a lab setting.
Relevant Majors: Chemistry, Material Science
This project investigates carbon capture and utilization through the Sabatier reaction, which converts CO2 and hydrogen into useful methane. The research focuses on synthesizing and testing a novel class of catalysts known as Hybrid Ultra-microporous Materials (HUMS), a specialized sub-category of Metal-Organic Frameworks (MOFs). A key distinction of HUMs is their ultra-microporous structure, with pores smaller than 0.7 nm, a property that promotes highly selective gas adsorption and separation. Students will gain hands-on experience by learning how to make and test these HUMs catalysts. This involves optimizing different synthesis methods and subsequently utilizing the materials in experimental CO2 methanation reactions to evaluate their catalytic activity, selectivity, and methane yield. The project includes comprehensive characterization of the catalysts using techniques such as SEM, TEM, XRD, XPS, BET, TGA, and FTIR to understand their behaviour and chemical changes throughout the process.
Relevant Majors: Chemical Engineering
The aim of the project is to investigate how easily products containing mercury are purchased, either online or in physical stores in Ireland. The focus includes two product categories known to regularly contain mercury despite regulatory restrictions: (1) Skin lightening/whitening creams and soaps, and (2) Traditional Asian medicines (TCMs) Mercury is commonly added to products for its melanin-inhibiting and whitening effects, and to certain TCMs for its purported calming and detoxifying effects. To assess market accessibility, the student(s) will: (a) Conduct a search of Irish-based online marketplaces and e-commerce platforms for products with descriptions including whitening, anti-age spot and lightening. (b) Visit ethnic shops to directly procure products with similar descriptions, (c) Visit alternative medicine outlets to procure products targeted for insomnia, anxiety, skin conditions and digestive issues – or any product listing cinnabar (mercury sulfide) as an ingredient. Samples will be tested for the presence of mercury using a handheld x-ray fluorescent (XRF) detector.
Relevant Majors: Chemical Engineering, Chemistry
Prostate cancer is the most common cancer among Irish men and a significant global health concern. Current early detection methods, such as PSA testing and digital rectal exams, lack precision, often leading to unnecessary biopsies and overdiagnosis. This study aims to improve risk assessment by analysing PSA measurements alongside clinical and demographic factors to more accurately stratify patients’ risk.
Statistical approaches, including logistic regression, mixed-effects models, and machine learning techniques, will be investigated to estimate individualised risk, with model performance evaluated and compared. Results will be translated into interactive tools (such as nomograms and Shiny apps) to support clinical decision-making and improve communication with non-technical audiences (see: https://shiny.posit.co/r/gallery/).
Relevant Majors: Public Health, Data Science
This project explores spatial patterns of food affordability concerns and deprivation across Limerick City and County using secondary data from the Food Finder 2025. Building on completed GIS mapping, the student will conduct statistical and thematic analysis to identify geographic clusters where food insecurity indicators (e.g., missed meals, affordability stress) are most prevalent. The project will also examine associations with transport access, housing status, and service use. Findings will inform local policy and community responses to food poverty. The student will produce a short report and presentation with recommendations for stakeholders such as the Limerick Food Partnership.
Relevant Majors: Public Health Nutrition, Social Policy
This project will explore the acceptability and perceived feasibility of implementing a newly proposed set of dietetic outcomes developed through a Delphi study involving practising dietitians in integrated geriatric care. The student will conduct a secondary analysis of open-text responses from the Delphi survey and/or design a short follow-up questionnaire or interview guide to assess how dietitians view the practicality of using these outcomes in routine clinical settings. The project will apply implementation science frameworks (e.g., CFIR or RE-AIM) to identify potential barriers and facilitators to adoption. The findings will inform future integration of dietetic outcomes into clinical documentation and service evaluation for older adults at risk of malnutrition.
Relevant Majors: Nutrition, Public Health
This project will undertake a comprehensive audit of health and wellbeing policies across the University of Limerick, contextualized within UL’s global leadership in Healthy Campus initiatives. Students will examine how the Okanagan Charter principles and the forthcoming Limerick Framework for Action are embedded across academic, administrative, and campus settings. The research will identify strengths, gaps, and opportunities to enhance policy alignment with international best practice, providing evidence-based recommendations for strategic improvement. Students will gain experience in qualitative policy analysis, stakeholder mapping, and comparative frameworks for higher education health promotion. Collaborative work with parallel projects on food insecurity and literature review will enrich understanding of holistic campus health strategies.
Relevant Majors: Public Health
This project explores the prevalence and impact of food insecurity among students and staff at UL, situating findings within national and international contexts. Students will gather and analyse data on food access, affordability, and equity, linking these insights to Healthy Campus initiatives, the IHPC, and UL’s leadership in advancing holistic wellbeing. The research will provide evidence to inform interventions and strategic planning under the Limerick Framework for Action, highlighting actionable pathways to address food insecurity in higher education. Students will work collaboratively with those conducting the policy audit and literature review, fostering an integrated approach to campus health and wellbeing.
Relevant Majors: Nutrition, Public Health
This project involves a systematic literature review to identify effective interventions addressing food insecurity in higher education and comparable populations. Students will synthesise international evidence to support the development of practical strategies for UL, aligned with the Okanagan Charter and the Limerick Framework for Action. Emphasis will be placed on translating research findings into actionable recommendations for campus policy, food provision programmes, and equity-focused interventions. The project will complement parallel student projects on policy auditing and food insecurity prevalence, fostering a collaborative, interdisciplinary approach. Students will gain experience in academic literature searching, critical appraisal, and synthesising findings to inform evidence-based practice in higher education health promotion.
Relevant Majors: Public Health, Health Sciences
Pain perception is a biopsychosocial experience that encompasses a variety of factors including wider biological influences such as menstrual cycle status. Menstrual cycle status can influence injury risk and the rehabilitation process following injury in female athletes. This project will involve completing focus groups and interviews to gather experiences of female athletes, coaches and support staff (medical and sport science) with relation to pain, injury and hormone profiles.
Relevant Majors: Physical Therapy, Sport Science
The sport of swimming commonly involves competing in numerous races and events both within and across a number of days. A warm up of appropriate length and structure may help to improve performance during racing. This project will involve designing an evidence based race warm up for Irish national team swimmers.
Relevant Majors: Physical Therapy, Sport Science
The margin between achieving a medal and not achieving a medal in international swimming is often less than a second. Recent evidence suggests that improving strength and power using resisted swimming may help a swimmer to perform at their best. This project will involve designing a resisted swimming training protocol using the 1080 Motion Sprint 2 system for Irish national team swimmers.
Relevant Majors: Physical Therapy, Sport Science
Much research has been done on the barriers to sports participation for girls. A 2021 systematic review on the barriers and facilitators of physical activity (PA) participation among girls highlighted that lack of peer, family and teacher support, along with lack of time were the most consistently cited barriers to PA (Duffey et al. 2021). Facilitators included weight loss and social support from peers, family and teachers. There is limited data on the motivators for team-based sports club participation amongst adolescents in Ireland. It is hoped that findings from the study will be used to help coaches understand why adolescents play sport. This may, in turn, influence appropriate coaching behaviours and ultimately have a positive impact on adolescent sport experiences.
Relevant Majors: Public Health, Psychology
Coaches are often described as the pivotal figures in choreographing conditions that support adaptive and resilient athlete performances in the complicated relationship between athlete and environment. In sport, and in particular high-performance sport the interactions of coaches and athletes are done in a complex system which is composed of many interacting parts or degrees of freedom, which need to be coordinated and continuously regulated in achieving task goals (Button et al, 2020). This study will examine coach leadership practices to understand how they contribute to athlete performance (Micro) and team cohesion (Meso) using a qualitative approach.
Relevant Majors: Sport Science
Peripheral serotonin has been identified to govern cellular functions. Serotonin enters cells through transport channels and enters nuclei to promote gene expression. This project involves studying the expression of serotonin transport channels and enzymes related to serotonin metabolism in cancer cells. The project will also explore if blocking serotonin entry and modulating metabolic enzymes may possess anti-cancer effects.
Recently, increased attention has been directed to the brain to better understand how motor skill expertise develops. One promising technique purported to accelerate motor skill improvement is transcranial direct current stimulation (tDCS). While simple fine motor tasks involving the hands and fingers are most frequently used to investigate the role of tDCS on motor skill learning, less work has examined the role of tDCS on complex sensori-motor tasks applicable to occupational, sport, and daily living activities. Esports require a high degree of sensori-motor control and have become one of the most popular forms of digital entertainment worldwide. Currently, little to no research has quantified the development of motor skill expertise in esports or whether tDCS can enhance skill improvement. The current study aimed to first differentiate the sensorimotor performance of a key gameplay skill among esports players of different skill levels.
Relevant Majors: Psychology, Neuroscience, Sport Science
Project ARISE (Activity and Recreation in an Inclusive Sensory Environment) brings people with a variety of expertise and from diverse backgrounds together to co-design an inclusive outdoor activity and recreation space to support health and well-being for all members of our society. The project has been developed in partnership between the Autism@UL Special Interest Group (SIG), Dóchas Midwest Autism Support and UL’s Age Friendly University Committee. It stems from a community-driven need, identified by Limerick parents of adults with disabilities who have noted the lack of physical activity spaces for their children. The activity needs of this population, met in childhood by accessible playgrounds, now go unmet. The aim of this project is to collaboratively develop a design proposal for an inclusive outdoor activity and recreation space for all members of our society. As well as offering exercise opportunities, and opportunities to connect with nature, the space will feature state of the art interactive interfaces, installations, visualisations and ambient displays.
Relevant Majors: Public Health
Ireland has an ageing population and many older adults in Ireland have low levels of physical activity and report loneliness and social isolation. Encouraging physical as well as social activity among older adults increases the overall quality of life, lowers all-cause mortality and reduces the incidence and effects of numerous illnesses and disabilities. Despite the benefits of community based social programmes, older males do not often participate or are reluctant to join. Related to this, older men are also unlikely to seek professional help when they are struggling with physical or mental health concerns. To date, little is known about community-based initiatives that focus on the social element of the health of older males. The overarching aim of this study is to qualitatively examine the motivations for joining the Men’s Shed movement in Ireland.
Relevant Majors: Public Health, Psychology
This project aims to investigate how the adipose tissue microenvironment influences the efficacy of novel hormonal therapies in breast cancer, with a specific focus on selective estrogen receptor degraders (SERDs). The study will examine the roles of the adipokines adiponectin and leptin in modulating estrogen receptor (ER) signalling and antiproliferative responses to SERD treatment in ER-positive breast cancer cells. Using treatment with recombinant adiponectin and leptin, we will assess changes in cell proliferation, apoptosis, and ER signalling activity. Analysis techniques will include viability assays, Western blotting and qPCR. These findings will be complemented by transcriptomic profiling to identify broader adipokine-mediated alterations in hormonal response networks.
As part of the assessment of painful conditions of the thumb and hand such as osteoarthritis, hand trauma, and carpal tunnel syndrome, clinicians measure static hand grip, and pinch grip, using validated and standardised dynamometers. This data can be used to assess severity of impairment of hand function, as well as for tracking the effectiveness of treatment when measured longitudinally. However, there is currently no validated or standardised clinical measurement of torque production of the thumb and fingers, despite the functional importance of this skill, and widespread reports from patients of difficulty with this function. This project aims to develop a standardised method of measuring grip torque, and collect data to generate a normative data profile for this measurement in adults across the lifespan. Project will involve participant recruitment, lab-based testing of grip torque, and quantitative analysis of the data.
Relevant Majors: Physical Therapy, Health Sciences
Progress in human research on glycolysis and muscle hypertrophy has been limited by the lack of robust, minimally invasive methods to assess the whole-body glycolytic power of a participant. Current glycolytic tests are either based on the analysis of glycolytic gene expression or enzyme activity of muscle biopsies or tests such as a Wingate test that do not only measure metabolism but biomechanical variables (Bar-Or 1987). We propose to use a new test vLapeak as a measure of muscle glycolytic capacity. We will use the vLapeak to answer the following question: Do more glycolytic individuals (i.e., individuals with a higher vLapeak) respond with more muscle hypertrophy to a standardised, hypertrophy-focused resistance training programme?
Relevant Majors: Exercise Physiology, Metabolism
The project is a Sequential Explanatory Mixed Methods Study designed to translate a novel finding from foundational qualitative research (that matriarchal budgeting skills impart non-cognitive capital) into a scalable, community-based intervention. Utilising an Action Research (AR) approach, the research will design and evaluate an 8-week financial literacy curriculum delivered by local female community leaders to post-primary students (ages 15-17) from four highly disadvantaged areas in Limerick City.
Relevant Majors: Community based interventions in education for inclusion & educational mobility.
Osteoarthritis (OA) is the most common chronic condition in those aged over 65 years. Self-management using physical activity (PA) is a core recommended treatment to offset progression, minimise the impact of pain and symptoms. The 8-week GLA:D® (Good Life with osteoArthritis in Denmark) programme, which is currently being implemented in Ireland, provides access to physiotherapist-supervised exercise and education for people with OA. While globally, GLA:D® has resulted in increases in self-reported PA, only 37% of those with low baseline PA reached moderate levels of PA at follow-ups. This project will be part of a larger pilot trial to measure the impact of different behaviour change interventions on physical activity maintenance, after participation in GLA:D. It will involve the analysis of subjective (questionnaire) and objective (accelerometry) measures of physical activity.
Relevant Majors: Physical Activity, Behaviour Change, Interventions
This project will examine the implementation of the Age-Friendly Health Systems (AFHS) framework—an evidence-based model of care developed in the United States through collaboration between the John A. Hartford Foundation and the Institute for Healthcare Improvement. The AFHS framework is designed to address the complex needs of older adults by focusing on four key areas of health and wellbeing, known as the 4Ms: What Matters, Medication, Mentation, and Mobility. Implementation will be examined in three long-term residential care centres in Ireland using a qualitative study design. Data will be collected through focus groups and semi-structured interviews with older adults and those important to them, and analysed thematically to explore their perceptions and experiences of care processes aligned with the 4Ms.
Relevant Majors: Ageing Research
Paramedicine has evolved and is now a complex network of allied health professionals working in a diverse range of roles as we move towards a future of integrated healthcare. The Irish Paramedicine Education & Research Network (IPERN) is dedicated to building research capacity and culture for out-of-hospital care in Ireland through engagement, education and collaboration. IPERN supports Irish paramedics to bridge the theory-practice gap through involvement in research training, knowledge generation, knowledge translation, evidence implementation, policy setting, research partnerships, co-production and research leadership. IPERN will be undergoing a 5 year evaluation in 2026 which will involve mixed methods research offering students the opportunity to participate in a number of projects and develop a broad range of research skills.
Relevant Majors: Public Health, Health Services Improvement, Emergency Medicine, Paramedicine
This project will collaboratively curate educational content for patients with musculoskeletal joint conditions for an existing web resource, and evaluate the impact of the website on health literacy, pain and quality of life. The website, JointPain.ie is a free educational web resource for people with conditions such as osteoarthritis that supports individuals to self-manage their pain with or without direction from a healthcare professional. The website has led to increasing demand to increase the number of conditions featured, requiring a collaborative process including healthcare professionals, patients and condition experts to co-design the content. Data gathered from website users will also be evaluated including impact on pain, quality of life and change in knowledge.
Relevant Majors: Musculoskeletal Pain, Educational Interventions, Health Communication
The following information is vetted and provided by the American Association of Collegiate Registrars and Admissions Officers (AACRAO) on the Electronic Database for Global Education (EDGE).
| Letter Grade | Percentage | Ranking | U.S. Equivalent |
| A+/A/A- | 70 - 100% | First Class Honours | A |
| B+/B/B- | 60 - 69% | Second Class Honours Upper | B+ |
| C+/C/C- | 50 - 59% | Second Class Honours Lower | B |
| D+ | 45 - 49% | Third Class Honours | C+ |
| D/D- | 40 - 44% | Pass | C |
| F | 0 - 39% | Fail | F |