Computational Science
The analysis layer across every dataset
Every HICS instrument produces time series — and turning raw readings into insight takes computation. This programme builds open machine-learning and analysis tools for environmental data: cleaning and gap-filling sensor records, detecting anomalies, and modelling the patterns in air, ground, and sky. Every tool is published openly, doubling as teaching material.
Time-series ML ▸
Models for environmental time series — forecasting, gap-filling, and separating signal from sensor noise.
Anomaly detection ▸
Flagging unusual readings automatically — feeding the data quality flags on the open dataset.
Open notebooks ▸
Analyses shipped as Jupyter notebooks built around real HICS data — also the backbone of the scientific computing courses.
Planned as a cross-cutting layer. It grows with the datasets — see the open data API and the scientific computing courses under Education.