Time-series Genetic Programming for Ice Sheet Models

Application process

A completed online application must be submitted by 4.30 pm 22 September 2025. Late or incomplete applications will not be accepted. Any required supporting documentation (including references) must also be received by 4.30 pm on the closing date in order for the application to be considered.

Project number

120

Project description

This summer research project will develop Time-series Genetic Programming (GP) approach for modelling ice sheet dynamics. The main goal is to predict ice thickness and ice velocity as time-series outputs, using climate and environmental input data. Unlike “black-box” machine learning models, GP produces interpretable solutions, making it possible to understand why certain predictions are made. The project will compare GP-based predictions with existing ice sheet simulations and observational data, evaluating both performance and scientific interpretability.

We are seeking a 3rd or 4th student with strong skills in machine learning and evolutionary computation. Proficiency in Python is required, and familiarity with evolutionary computation or climate data will be advantageous.

The student will be supervised by a multidisciplinary team including Dr. Bach Nguyen and Prof. Bing Xue (School of Engineering and Computer Science) and Dr. Peter Siew (Antarctic Research Centre).

This project is for a single student.