Machine Learning
computational skill
Build predictive models from scientific data — classification, regression, clustering, and model evaluation.
Machine learning in scientific contexts uses algorithms to find patterns in data, predict outcomes, classify observations, and discover relationships that would be difficult to detect manually. Research Atlas focuses on interpretable, scientifically defensible models rather than black-box approaches.
Where Machine Learning is used
Careers using Machine Learning
Environmental Data Scientist
Applies data science, GIS, and statistical methods to environmental problems — monitoring ecosystems, modelling climate impacts, and informing environmental policy.
Climate Data Scientist
Analyses climate datasets, models future scenarios, and communicates climate risk.
Scientific Data Scientist
Applies data science methods across scientific disciplines to extract insight from experimental and observational data.
Scientific Machine Learning Engineer
Builds machine learning systems for scientific applications — from research prototypes to production scientific software.
Agritech Data Scientist
Applies remote sensing, GIS, and machine learning to precision agriculture and food systems.