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Courses
Courses
Choosing a course is one of the most important decisions you'll ever make! View our courses and see what our students and lecturers have to say about the courses you are interested in at the links below.
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University Life
University Life
Each year more than 4,000 choose University of Galway as their University of choice. Find out what life at University of Galway is all about here.
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About University of Galway
About University of Galway
Since 1845, University of Galway has been sharing the highest quality teaching and research with Ireland and the world. Find out what makes our University so special – from our distinguished history to the latest news and campus developments.
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Colleges & Schools
Colleges & Schools
University of Galway has earned international recognition as a research-led university with a commitment to top quality teaching across a range of key areas of expertise.
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Research & Innovation
Research & Innovation
University of Galway’s vibrant research community take on some of the most pressing challenges of our times.
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Business & Industry
Guiding Breakthrough Research at University of Galway
We explore and facilitate commercial opportunities for the research community at University of Galway, as well as facilitating industry partnership.
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Alumni & Friends
Alumni & Friends
There are 128,000 University of Galway alumni worldwide. Stay connected to your alumni community! Join our social networks and update your details online.
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Community Engagement
Community Engagement
At University of Galway, we believe that the best learning takes place when you apply what you learn in a real world context. That's why many of our courses include work placements or community projects.
Accessibility & LanguageProjects
Nationally Funded
List of all projects that receive Funding from National bodies
Project Title: Artificial intelligence-powered 3D printing (aiPRINT)
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- Funding Agency: Research Ireland – National Challenge Fund (Concept, Seed &Grow Phase)
- Start and End Dates: 01/07/2023 – 30/06/2025
- PI/Co-PI: Co-Investigator Researchers Involved: Dr. Karl Mason, Dr Andrew Daly, Dr. Vasileios Sergis, Dr. Daniel Kelly, Dr. Usman Haider, Lukasz Szmet
- Webpage Link: https://www.sfi.ie/challenges/future-digital/aiprint/
- Brief Description: This project advances biofabrication by using computer vision to detect 3D printing errors as they occur and reinforcement learning to correct extrusion errors.
- UNSDGs Addressed: 9, 12
- Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Computer vision & Robotics
Project Title: Augmented Reading Room for Radiologists
- Funding Agency: Insight Research Ireland Center for Data Analytics
- 2024 – 2025
- PI: Dr Ihsan Ullah
- Webpage:
- Description:. The objective of this project is to develop a simple platform that enhances the radiologist’s workflow using AR/VR technology. The proposed “Augmented Reading Room” will enable radiologists to directly interact with research-level AI models without requiring complex or time-consuming integration with hospital PACS systems. This platform is not intended to replace or compete with existing diagnostic tools already available in hospital systems. Instead, it aims to complement them by supporting clinical decision-making and adding a crucial layer of interpretability and explainability, which is often lacking in current AI solutions. Ultimately, the AI4MI “Augmented Reading Room” will facilitate and accelerate the adoption of research-level AI models in the field of radiology.
Project Title: Anatomical Part Segmentation of Newborn Skeleton
- Funding Agency: Insight Research Ireland Center for Data Analytics
- 2024 – 2025
- PI: Dr Ihsan Ullah
- Webpage:
- Description:. Accurate and fully articulated modeling of the fetal skeleton plays a crucial role in enhancing childbirth simulations, enabling better prediction of delivery outcomes, and supporting clinical decision-making during labor. However, the collection of high-quality fetal imaging data poses significant challenges due to ethical, technical, and safety constraints. In contrast, neonatal imaging data are more accessible and can serve as a viable proxy for studying and modeling the fetal skeletal structure. This project aims to develop a geometric deep learning framework capable of automatically segmenting and reconstructing the neonatal skeleton from medical imaging data. Ultimately, this work contributes to the broader goal of improving biomechanical simulations of childbirth, facilitating more precise prediction of complications, and paving the way for personalized obstetric care through data-driven modeling of fetal and neonatal anatomy.
Project Title: Effective Integration of Renewable Energy within the Agriculture Sector in Ireland using Artificial Intelligence (EIRE AIAI)
- Funding Agency: Research Ireland – Frontiers for the Future Programme
- Start and End Dates: 01/07/2022 – 30/06/2027 (60 months)
- PI/Co-PI: Principal Investigator and Lead Applicant
- Researchers Involved: Dr. Karl Mason, Dr. Abdul Wahid, Dr. Marcos Cruz, Dr. Junlin Lu, Nawazish Ali, Hossein Khaleghy, Mian Shah, Iias Faiud
- Webpage Link: https://www.autonomous-agents-research.com/research
- Brief Description: This project proposes using Artificial Intelligence methods to effectively integrate renewable generation into dairy farming, by combining it with recent technological developments in the energy sector.
UNSDGs Addressed: 7, 9, 11
Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Smart Infrastructure
Project Title: Personalised Sensory Regulation: Assessing Biometric Wearable Integration in School-based CUBBIE Sessions
- Funding Agency: Data2Sustain EDIH Program June 2025 – May 2026
- PI:Dr. Frank Glavin; Dr. Attracta Brennan. Researchers Involved: Damian Gonzalez Garza
- Webpage:
- Description: This project is a collaboration with Cubbie, a company that develops self-contained, immersive booths that help people, especially those who are autistic or neurodivergent, manage sensory overload by providing a private and calming space. These pods use personalised and adjustable settings like lighting, sound, and visuals to create either a stimulating or calming environment, which can help reduce stress and anxiety. The first phase of this project (June 2025 - November 2025) involved a full data analysis of Cubbies records and planning for biometric device integration. The second phase (December 2025 – May 2026) involves running a pilot study of the integration of a comprehensive biometric wearable in a school setting.
- Funding Agency: Research Ireland (previously Science Foundation Ireland) National Challenge Fund
- 2023 – 2025
- PI: Dr Ihsan Ullah, Co-PI: Dr Waqar Shahid Qureshi
- Webpage: https://www.paveanalytics.eu/
- Description:. Visit the website for details.
Project Title: Molecular Programming for Designing Bio-molecular Computers
- Funding Agency: College of Science & Engineering Strategic (Millenium) Research and Innovation Fund. Sep 2023 – Aug 2025
- PI: Principal Investigator
- Webpage:
- Description:. We are developing a molecular-programming toolbox for the de novo design of DNA hexahexaflexagon (and related context-switchable nanostructures) capable of simple computation. Constraint-aware generative models learn sequence to structure and then structure to function motifs in order to assemble candidates under explicit Watson–Crick pairing and hierarchical self-assembly rules. Designs are triaged in silico using thermodynamic/kinetic analysis and coarse-grained simulation to select foldable, switchable geometries, then fabricated and assessed in vitro by AFM and standard biophysical read-outs to verify state transitions and logic. The project will deliver an open-licence software package and a step-by-step protocol, released as a web service, to standardise and accelerate design–build–test cycles towards diagnostic and therapeutic nanosystems.
Project Title: Dreamtec Software Ltd T/A Dreamtec System. Innovation Boost powered by Fusion
- Funding Agency: InterTradeIreland. Sept 2020 – Mar 2022.
- PI:
- Webpage:
- Description: An 18-month industry–academic project with Dreamtec Systems to build two production-grade capabilities; an analytics engine that turns a proprietary telemetry solution (metered volumes, GPS, timings, routes) into actionable insights—trend analysis, demand forecasting, fleet/productivity KPIs and market dashboards; and then an intelligent, self-learning parser that ingests heterogeneous meter formats without manual templates, enabling faster, more accurate onboarding at scale. Both components will be exposed as APIs and dashboard plug-ins on Dreamtec’s platform to reduce support overheads and unlock upgraded subscription services for customers.
Project Title: Charlemont Grant (Royal Irish Academy)
- Funding Agency: Royal Irish Academy – Charlemont Grant
- Start and End Dates: 25/03/2022 – 25/11/2022 (8 months)
- PI/Co-PI: Principal Investigator
- Researchers Involved: Dr. Karl Mason, Prof Sabine Hauert
- Webpage Link: https://www.autonomous-agents-research.com/research
- Brief Description: This project focuses on using multi-objective evolutionary methods to develop controllers for swarm robotics. This research was funded by the Royal Irish Academy and was conducted in collaboration with Prof. Sabine Hauert at the Bristol Robotics Laboratory, UK.
- UNSDGs Addressed: 9
- Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Computer vision & Robotics
Internationally Funded
List of all projects that receive Funding from international bodies
Project Title: An Integrated Graph Theoretical Substructure Similarities Searching Algorithm for Drug Repositioning and Off-Target Toxicity Assessments using Antimicrobial Resistance Model
- Funding Agency: Ministry of Higher Education, Malaysia – Translational Research Grant. Dec 2022 – Nov 2025
- PI:Co-PI.
- Webpage:
- Description:. We are developing a graph-based 3D substructure-similarity workflow to identify repositionable drugs from approved-drug libraries while flagging probable human off-targets, using antimicrobial resistance as the model system. Binding-site motifs are encoded as residue/atom graphs with explicit geometry and systematically interrogated across bacterial and human proteomes (PDB/AlphaFold) using tolerance-aware subgraph isomorphism. High-scoring candidates are prioritised with lightweight docking and ADMET filters, then progressed to focused experimental validation on curated AMR panels. The project will deliver a reproducible toolkit and web service that broaden discovery beyond exact-match queries while reducing computational cost and turnaround time.
Project Title: Innovative AI Solutions to Support Trustworthy Online Activity
- Funding Agency: (Horizon EU) Start/end dates (January 2024 - December 2027)
- PI:
- Webpage: https://ai4debunk.eu
- Description: (Recognizing the persistent and evolving nature of disinformation, AI4Debunk focuses on the symbiotic relationship between humans and advanced AI tools. Our innovative approach involves bridging the sociological aspects of disinformation with concrete AI-based solutions to deter it. Through AI4Debunk, users will gain access to resources, knowledge, and skills, empowering them to detect disinformation in the ever-changing digital landscape. Our priority is to develop user-friendly and inclusive tools to reach individuals of all ages, genders, interests, and online environments.))
