This course is available through the following application route(s)
Course Description
The MSc Artificial Intelligence for Weather & Climate Change is a 1-Year, full-time course that integrates artificial intelligence, meteorology, and climate science to equip graduates with advanced data-driven and modelling skills for understanding and predicting weather and climate phenomena. Co-delivered with Met Éireann, the Irish Meteorological Service, the programme combines rigorous theoretical foundations with practical training in AI, machine learning, and numerical modelling. Students learn to design and implement AI systems for environmental data, forecast extremes, and assess climate impacts.
The course prepares graduates to contribute to cutting-edge research and to address real-world challenges in climate forecasting, disaster mitigation, and environmental policy. Graduates will be uniquely positioned to apply AI in global weather and climate contexts.
Scholarships and Stipends
UCD School of Mathematics & Statistics and Met Éireann are offering two highly competitive funding opportunities for EU and non-EU applicants.
Graduates will gain highly sought-after skills at the interface of AI, meteorology, and climate science, positioning them for roles in national meteorological services, renewable energy, environmental consultancy, and climate technology sectors. Typical roles may include data scientist, climate modeller, AI researcher, or environmental analyst. Organisations such as Met Éireann, The European Centre for Medium Range Weather Forecasting, and climate-focused start-ups are potential key employers. Graduates will also be well prepared for PhD-level research in AI and atmospheric science, contributing to innovations in weather prediction, sustainability, and climate adaptation.
Below is a list of all modules offered for this degree in the current academic year. Click on the module to discover what you will learn in the module, how you will learn and assessment feedback profile amongst other information.
Incoming Stage 1 undergraduates can usually select an Elective in the Spring Trimester. Most continuing undergraduate students can select up to two Elective modules (10 Credits) per stage. There is also the possibility to take up to 10 extra Elective credits.
| Trimester | Credits | |||
|---|---|---|---|---|
Stage 1 Core Modules |
ACM41070 | Physical Meteorology & Climate | Autumn | 5 |
Stage 1 Core Modules |
ACM41090 | Synoptic Meteorology | Autumn | 5 |
Stage 1 Core Modules |
STAT41140 | AI for Weather and Climate | Autumn | 10 |
Stage 1 Core Modules |
ACM41100 | Computing for Weather app | Spring | 5 |
Stage 1 Core Modules |
ACM41960 | Physical Met Field Trip | Spring | 5 |
Stage 1 Core Modules |
STAT41150 | Extremes for Climate | Spring | 5 |
Stage 1 Core Modules |
STAT41160 | Impacts for Weather & Climate | Spring | 5 |
Stage 1 Core Modules |
ACM41120 | Dissertation | Summer | 30 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
ACM30220 | Partial Differential Equations | Autumn | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
CVEN40830 | Applied Hydrology | Autumn | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
GEOG40960 | Coastal Risks | Autumn | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
GEOG41090 | Urban Data Analytics | Autumn | 10 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
GEOL40710 | Natural hazards and risk | Autumn | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
GEOL40740 | Marine Geoscience | Autumn | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
COMP47670 | Data Science in Python (MD) | Autumn and Spring (separate) | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
COMP47750 | Machine Learning with Python | Autumn and Spring (separate) | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
ACM41000 | Uncertainty Quantification | Spring | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
ACM41110 | Dynamical Met & NWP | Spring | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
GEOG20060 | Weather, Climate and Climate Change | Spring | 5 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
GEOG40890 | Remote Sensing | Spring | 10 |
Stage 1 Options - A) Min 4 of: Students take 20 credits |
GEOG41060 | Geostatistics and Programming for GIS | Spring | 10 |
Autumn Trimester - Core modules: ● Physical Meteorology & Climatology ● AI for Weather and Climate ● AI & Extremes for Weather and Climate ● Synoptic Meteorology Option modules: ● Remote Sensing ● Urban Data Analytics ● Marine Geoscience ● Natural Hazards and Risks ● Applied Hydrology ● Machine Learning with Python |
Professor Andrew Parnell is the Met Éireann Professor of Data Science for Climate and Weather at University College Dublin and Director of the AIMSIR research institute for Artificial Intelligence in Meteorological Services, Innovation and Research. His research is in AI, machine learning and statistical modelling applied to many different weather, climate and ecological areas. He is currently Principal Investigator and Deputy Director of the Research Ireland Co-Centre in Climate, Biodiversity and Water, and a funded investigator in the SFI Insight Centre for Data Analytics.
Applicants must hold a 2:1 honours degree (or international equivalent) in a science, mathematics, engineering, or cognate discipline, with prior study of calculus and linear algebra. Other relevant backgrounds include meteorology, environmental science, computer science, physics, or data analytics.
Applicants must meet the minimum english language requirements.
Full Time option suitable for:
Domestic(EEA) applicants: Yes
International (Non EU) applicants: Yes
General application route(s) for Irish/UK/EU applicants* for International (non-EU) applicants* to MSc Artificial Intelligence for Weather & Climate Change FT:
| ROWCLASS | Apply to |
|---|---|
| showAudience-audienceEU showAudience-audienceInt | F297 MSc Artificial Intelligence for Weather & Climate Change FT Master of Science Full-Time Commencing September 2026 Graduate Taught |