ICCS Summer Internships: the projects
We're looking for interns to work on these projects this summer as part of the Institute of Computing for Climate Science.
Accelerating UKCA using data-driven timestep adaptation
The chemistry solver within the UKCA model – called ASAD – has been established as its most computational intensive component. As such, it is a key target for computational optimisation.
Geospatial Foundation Models
Through partnerships providing access to extensive field datasets, this project aims to produce new, continent-scale maps of habitats and temporal changes. Such work holds significant value for practitioners and policymakers striving to manage the planet’s resources more sustainably.
Constraining a terrestrial ecosystem model using greenhouse gas flux and remote sensing data
The project will involve compiling and processing existing greenhouse gas flux data, as collected within the Centre for Landscape Restoration (CLR) project and from other sources, and using them to test and parameterise the ecosystem dynamics model, HYBRID, across the three CLR landscapes.
Symbolic execution of Fortran programmes
The aim of this internship is to extend CamFort with symbolic execution capabilities. Symbolic execution is a powerful programme analysis technique that systematically explores programme paths using symbolic inputs, enabling the detection of subtle and deep-seated bugs in complex codebases.
Combining observations and machine learning to infer ocean turbulence
Previous work has shown that machine learning models can be trained on the existing turbulence data and be used to infer turbulent mixing from observations of T, S, Z measured routinely by global observational programs. In this project, we aim to expand that work, and apply the trained algorithm to existing datasets.
