26 May to 21 July, 2027
For further information or enquiries:
About
The International Summer Research Programme at the University of Limerick is a fee-paying programme that offers students the opportunity to gain practical research experience in a supportive academic environment.
Students will work on a research project of their choice under the guidance of experienced mentors and academic staff, using UL’s modern laboratory and research facilities. The programme also includes student-led social and cultural activities designed to complement the academic experience.
As of right now, STEM research projects are credit-bearing, while EHS (Education, Health, and Social Sciences) projects are currently not credited by UL. We are, however, happy to work with your home university to facilitate the awarding of credit. We recommend you reach out to your home university Study Abroad Office about the possibility of receiving credit.
Summer Research Projects Available
STEM Research Projects (Credited by UL)
| Project Title | Research Field | Requirements | PA |
|---|---|---|---|
| AI-Driven Gameplay Video Analysis for Intelligent Esports Coaching and Performance Feedback | Artificial Intelligence, Computer Vision, Machine Learning, Esports Analytics, Human-Centred AI, Data Science | Essential Requirements Undergraduate degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related discipline. Programming experience in Python. Basic understanding of machine learning concepts. Familiarity with data analysis and software development. Desirable Requirements Experience with computer vision libraries such as OpenCV. Knowledge of deep learning frameworks (e.g., PyTorch or TensorFlow). Interest in gaming, esports, or video analytics. Experience working with datasets and experimental evaluation. Familiarity with Git and collaborative software development. | 2 |
| Trap, Concentrate, Convert: Core-Shell materials to turn pollutants into valuable products | Chemical Engineering, Chemistry, Catalysis | Major in Chemical Engineering/Chemistry/Material Science. General experience with labs. Careful lab practice. | 1 |
| Making Every Atom Count: Oxide monolayers for enhanced dispersion | Chemical Engineering, Chemistry, Catalysis | Major in Chemistry/Chemical Engineering/Material Science. General experience with labs. Careful lab practice. | 1 |
| TRACE-AI: Visualising Agentic AI for Transformer-Based Cyber Threat Detection | Cyber, AI, Visualisation | Software development, AI, Cyber | 1 |
| Using Alternative Proteins to develop plant based food and beverages | Biochemistry | Basic knowledge in food chemistry | 3 |
| Characterisation of Dielectric Fluids for Immersion Cooling | Mechanical Engineering | Heat transfer, thermodynamics, fluid mechanics | 1 |
| Characterisation of endometrial tissue across reproductive cycles | Biomedical Engineering / Women's Health | Student undertaking a degree in biomedical engineering/cell and molecular biology/biochemistry or related discipline. | 2 |
| Pulsating Heat Pipe (PHP) development and characterisation | Mechanical Engineering | Heat transfer, thermodynamics, fluid mechanics | 1 |
| Developing Lipid-Based Materials for Biomedical 3D Printing | Materials Science, Chemical Engineering | Organic chemistry, synthesis, | 1 |
| DAGger: Directed Acyclic Graph generation | Combinatorial enumeration; DAG generation; Algorithms; Computer Science | Essential: strong understanding of algorithms and data structures; strong coding abilities; Desirable: familiarity with a functional or high-level programming language. No Java programmers need apply :-) | 2 |
| What if removing part of a battery made it better? Exploring anode-free Li batteries. | Energy Storage | Major in Chemistry/Chemical Engineering/Material Science/Physics. | 1 |
| Improving Software Quality Through Research Software Engineers’ Daily Practices | Software Engineering | Essential: Interest in topic; Desired: Software engineering, research methods, coding of interviews | 2 |
| Macrophage phenotyping in a 3D spheroid model of endometriosis | Molecular Biology | Basic knowledge of cell culture and molecular biology techniques such as PCR and sequencing. | 2 |
| Investigating political attitude networks to explore polarization dynamics and their implications for political behavior | Political Psychology, Belief Network Analysis | Majors in social psychology or political science with an active interest in public opinion, polarization, and political behavior research. Prior programming skills in R or Python and skills in statistical network analysis are a plus. | 2 |
| Profiling Breast Cancer’s Molecular Response To A Novel Epigenetic Anticancer Therapeutic Using Next-Generation Sequencing | Molecular Biology/Cancer | Desirable requirements: Experience in molecular biology techniques, experience with data analysis and research skills, strong background in academic writing and communication, time and project management. | 2 |
| AI-Assisted Detection of Software Maintainability Problems in ATLAS | Software Engineering | Essential: Students should have a sound understanding of object-oriented programming, software design principles and software development practices. Proficiency in Python and familiarity with Git, automated testing and basic software quality concepts are required. Students should be able to read and understand unfamiliar source code and have experience developing and testing software applications. Desirable skills or experience: Knowledge of software architecture, SOLID principles, static analysis tools and ISO/IEC 25010 would be advantageous. Experience with large language models, prompt engineering, C++ or open-source software development would also be beneficial. Students should have completed at least two years of undergraduate study in a relevant computing discipline or possess equivalent practical experience. Students should demonstrate the ability to work independently, investigate unfamiliar technical problems, communicate research findings and maintain a structured record of their experimental work. Prior knowledge of particle physics, CERN or the ATLAS experiment is not essential. Relevant scientific background will be introduced through the project orientation and supervision. | 1 |
| Machine Learning for Predicting Software Test Failures in ATLMachine Learning for Predicting Software Test Failures in ATLAS | Software Engineering | Essential: Students should be proficient in Python and have a basic understanding of software testing, version control and software development workflows. Familiarity with fundamental machine learning concepts, including supervised learning, classification and model evaluation, is required. Students should also possess basic statistical knowledge and experience manipulating structured datasets. Desirable skills or experience: Experience with Scikit-learn, Pandas, automated testing frameworks and continuous integration systems would be advantageous. Familiarity with Git repository analysis, feature engineering, regression testing and software test prioritisation would further support the research. Students should have completed at least two years of undergraduate study in a relevant computing discipline or possess equivalent practical experience. Students should demonstrate the ability to work independently, investigate unfamiliar technical problems, communicate research findings and maintain a structured record of their experimental work. Prior knowledge of particle physics, CERN or the ATLAS experiment is not essential. Relevant scientific background will be introduced through the project orientation and supervision. | 1 |
| Explainable AI for Detecting Anomalies in ATLAS Detector Monitoring Data | Software Engineering | Essential prerequisites: Students should be proficient in Python and have foundational knowledge of machine learning, statistics and data analysis. Experience preparing, analysing and visualising numerical datasets is required, together with an understanding of basic classification and model evaluation techniques. Students should be comfortable working with scientific datasets and interpreting quantitative results. Desirable skills or experience: Familiarity with anomaly detection algorithms, unsupervised learning, Scikit-learn and explainable AI techniques such as SHAP would be advantageous. Experience with time-series analysis, scientific computing or sensor-monitoring applications would provide additional preparation. Students should have completed at least two years of undergraduate study in a relevant computing discipline or possess equivalent practical experience. Students should demonstrate the ability to work independently, investigate unfamiliar technical problems, communicate research findings and maintain a structured record of their experimental work. Prior knowledge of particle physics, CERN or the ATLAS experiment is not essential. Relevant scientific background will be introduced through the project orientation and supervision. | 1 |
| AI-Assisted Performance Optimisation of Scientific Computing Workloads | Software Engineering | Essential: Students should have strong Python programming skills and a sound understanding of algorithms, data structures and computational complexity. Familiarity with operating-system fundamentals, memory management and basic performance measurement is required. Students should also possess introductory knowledge of machine learning and sufficient statistical understanding to interpret experimental performance measurements. Desirable skills or experience: Experience with performance profiling tools, NumPy, parallel programming, C++ or GPU computing would be advantageous. Knowledge of high-performance computing, CUDA, resource optimisation or machine learning regression techniques would further support the research. Students should have completed at least two years of undergraduate study in a relevant computing discipline or possess equivalent practical experience. Students should demonstrate the ability to work independently, investigate unfamiliar technical problems, communicate research findings and maintain a structured record of their experimental work. Prior knowledge of particle physics, CERN or the ATLAS experiment is not essential. Relevant scientific background will be introduced through the project orientation and supervision. | 1 |
| AI-Assisted Validation of Scientific Simulation Software | Software Engineering | Essential prerequisites: Students should be proficient in Python and have a foundational understanding of software testing, statistical analysis and machine learning. Experience manipulating numerical datasets, producing scientific visualisations and interpreting statistical results is required. Students should understand basic hypothesis testing and be capable of designing and evaluating reproducible computational experiments. Desirable skills or experience: Familiarity with SciPy, Scikit-learn, Monte Carlo simulation, anomaly detection and statistical distribution comparison would be advantageous. Previous experience with scientific simulation software, numerical modelling, Geant4 or computational physics would provide useful additional preparation. Students should have completed at least two years of undergraduate study in a relevant computing discipline or possess equivalent practical experience. Students should demonstrate the ability to work independently, investigate unfamiliar technical problems, communicate research findings and maintain a structured record of their experimental work. Prior knowledge of particle physics, CERN or the ATLAS experiment is not essential. Relevant scientific background will be introduced through the project orientation and supervision. | 1 |
| AI-Assisted Redesign of the ATLAS JetUncertainties Tool | Software Engineering | Essential: Students should have a sound understanding of object-oriented programming, software design principles and software testing. Experience programming in C++ is required, together with familiarity with Python, Git and basic software development workflows. Students should be able to understand unfamiliar source code, identify dependencies and implement changes while preserving existing functionality. Desirable skills or experience: Knowledge of SOLID and GRASP principles, software architecture, refactoring techniques and ISO/IEC 25010:2023 would be advantageous. Experience with static analysis tools, automated regression testing, large language models, AI-assisted development tools or scientific computing frameworks would provide additional preparation. Students should have completed at least two years of undergraduate study in a relevant computing discipline or possess equivalent practical experience. Students should demonstrate the ability to work independently, investigate unfamiliar technical problems, communicate research findings and maintain a structured record of their experimental work. Prior knowledge of particle physics, CERN or the ATLAS experiment is not essential. Relevant scientific background will be introduced through the project orientation and supervision. | 1 |
| iDaVIE+: immersive Data Visualisation Interactive Explorer | Software Engineering | Essential: The student should have a strong foundation in object-oriented programming, software engineering principles and software architecture, together with practical programming experience in C#. Familiarity with software design principles, including SOLID, modularity and separation of concerns, is required. The student should be comfortable working with existing codebases, using Git for version control, and developing and executing software tests. An ability to analyse technical problems, work independently and communicate findings clearly is essential. Desirable skills: Experience with Unity, client-server architectures, API development and scientific visualisation would be advantageous. Familiarity with software quality standards, particularly ISO/IEC 25010, and static analysis tools such as SonarQube, NDepend or CodeScene would be beneficial. Knowledge of software architecture modelling using UML or C4, automated testing, continuous integration and performance benchmarking is desirable. An interest in research software engineering, scientific computing or astronomy would be particularly valuable. | 1 |
| Breast Cancer prediction using Machine Learning Methods | Statistics, Machine Learning, Cancer Research | Knowledge of statistics and statistical programming (R) are expected. | 2 |
| Development of an Ex Vivo Concussion Model | Engineering (Biomedical or Mechanical or Design) | Necessary: Engineering, optional: design methods, machining skills, | 3 |
Education and Health Sciences Projects (Not Credited by UL)
| Project Title | Research Field | Requirements | PA |
|---|---|---|---|
| Boredom and the Boring People Stereoetype | Social Psychology | Knowledge of experimental designs and quantitative analyses in Psychology | 2 |
| Optimising race warm up strategies for elite swimmers | Sport Science | Ideally students from year 2-4 on an undergraduate or masters programme in sports science,kinesiology or a similar field | 1 |
| Designing a resisted swimming training intervention for elite swimmers | Sport Science | Ideally students from year 2-4 on an undergraduate or masters programme in sports science,kinesiology or a similar field | 1 |
| An Exploration into Young Carers' Experience of Advance Care, and Future Planning whilst Caring for a Family Member. | Palliative Care: Nursing, Medicine, Social Studies. Education | 3rd or 4th year UnderGrad or any year Post Grad Students from disciplines: Nursing, Midwifery, Education, Public Health, Medicine, Social Studies, Psychology. ESSENTIAL: A level of understanding on the scoping literature review process, interest in either palliative care, family caregiving, child and adolescent wellbeing, health and social care research. Strong academic writing, organisational and interpersonal skills, Ability to work independantly and manage project timelines. DESIRABLE: previous experience completing a literature review and thematic analysis. | 2 |
| Stigma, isolation and loss in COPD: Understanding the psychosocial costs of chronic respiratory disease. | Any health science or social science | The candidate requires strong qualitative research skills (data collection and thematic analysis), exceptional interpersonal and communication abilities to engage sensitively with patient groups, and excellent academic writing. They must be capable of working independently to manage project timelines and produce clear research outputs. | 2 |
| Perception vs Reality: comparing adolescent motivations for sports participation with coaches' perceptions | Coaching in Adolescent Sport | This project uses a mixed methods approach and would suit a 2nd or 3rd year student of Sport and Exercise Sciences, Kineseology and potentially, Sociology. A basic understanding of statistical packages (SPSS, excel) and thematic analysis would be helpful. | 2 |
| Using many models in health decision making | Biostatistics, health data science | Essential: knowledge of applying statistical regression models to data. Desriable: experience of statistical programming (e.g., R) | 2 |
| Prostate Cancer Risk Prediction and Interactive Decision-Support Tools | Health Data Science, Clinical Decision-Support Tools, and Cancer Research | Basic knowledge of statistics and an interest in health data science are expected. Experience in statistical programming (e.g., R or Python), machine learning, or interactive application development would be an advantage. | 2 |
| Childhood adversity, PTSD and stress | Health Psychology | Knowledge of experimental designs and quantitative analyses in Psychology. Desirable: understanding of stress testing protocols, DINAMAP equipment. | 2 |
| Structural elucidation of a novel lipid extract from salmon | Chemistry, Food Science | Analytical Chemistry, Biochemistry | 1 |
| Crystal Engineering for Clean Air: Azolate Networks for Toxic Gas Removal | Chemistry, Materials Science | Essential: Undergraduate students in Years 2–4 of a Chemistry or Materials Science programme, or students enrolled in a related Master’s programme. Desirable: Some laboratory experience in chemical synthesis or materials characterisation, and an interest in learning about porous materials, crystal engineering, and gas adsorption. | 1 |
| PRESAFE: Safety, Sustainability and Accessibility in Remote Nurse Prescribing across NPA Regions | Medicine, health data science | Year 3 or 4 or postgraduate student in medicine, health sciences or psychology. Strong qualitative research skills (data collection and analysis). Strong academic writing, organisational and communication skills. Ability to manage project timelines and produce clear research outputs. | 2 |
| Adolescent preferences for Coach communication in adolescent team spots. | Sport development | This project uses a mixed methods approach and would suit a 2nd or 3rd year student of Sport and Exercise Sciences, Kineseology and potentially, Sociology. A basic understanding of statistical packages (SPSS, excel) and thematic analysis would be helpful. | 2 |
| Understanding Physical Preparation and Coach Learning within a National Athletics Performance Pathway | Sport Coaching; Athlete Development; Strength and Conditioning; Sport Science | Essential: Year 2–4 undergraduate student in Sport and Exercise Science, Sport Coaching, Psychology, Education or a related discipline. Strong academic writing, critical thinking and communication skills, with an interest in coaching research and athlete development. Desirable: Experience with survey research, quantitative or qualitative data analysis, Microsoft Excel or statistical software (e.g., SPSS or R). | 1 |
| Exploring pre-service teachers’ priorities and visions for change in physical education | Education: physical education teacher education | This project would suit students studying education, particularly physical education. Related disciplines such as kinesiology, sociology, or social sciences might also be suitable. Essential: interest in and some familiarity with qualitative research. | 2 |
| Measuring self-reported comics knowledge | Psychology, art, literature | Students with a background in psychology, art, or literature with an awareness of quantitative research methods would be ideal. Interest in comics would also be beneficial. | 2 |
| Teaching Policy in Teacher Education: Mapping Current Approaches and Practices | Education: educational policy, higher education | This project would suit students studying education, public policy/administration, political science. Essential: interest in and some familiarity with policy and qualitative research. | 2 |
| An exploration of the impact of media coverage of teaching on teachers and school leaders in Ireland | Education | Completing a degree in Education and interest in the topic. Experience of engaging with literature reviews, knowledge of qualitative research methods | 1 |
| An analysis of deprescribing interventions and shared decision making | Healthcare professional and in particular nurse prescribers,prescribing, polypharmacy, decision making and older adults. | This project may advance the skills and the steps involved in conducting a review of the literature systematically.This project will include contributing to the completion of an integrated review and advance knowledge on learning how to publish. | 2 |
| Multimorbidity and Self-Rated Health Among Older Adults in Ireland: The Mediating Role of Social, Functional and Behavioural Factors Using TILDA Data | Biostatistics, Quantitaive data analysis | Undergraduate/Master student in Public Health, Epidemiology, Statistics, Psychology, Health Sciences or a related discipline. Basic understanding of quantitative research, good academic writing skills and an interest in population health research. Experience with statistical software (Stata, R or SPSS), regression analysis, literature reviews and secondary data analysis | 1 |
How to Apply for a Research Project
STEM Research Projects
If you are interested in applying for a STEM research project, please submit the project title, a letter of interest, and your CV to summer.schools@ul.ie
Your application will be reviewed by the Principal Investigator of the project, who will determine whether to accept or reject your application. You will typically receive a response within two weeks of submission. Once accepted, you may proceed to formally apply for the programme through the UL application portal, found at the bottom of the main International Summer Programmes page.
Education and Health Sciences
If you are interested in an EHS research project, please first contact your Study Abroad Office to discuss the possibility of being awarded academic credit by your home university, as these projects are not currently credited by the University of Limerick.
If your home university agrees to award credit, you can then submit the project title, a letter of interest, and your CV to summer.schools@ul.ie
The Principal Investigator will then review your application and inform you of their decision, typically within two weeks of submission.
