Discovery Early Career Researcher Award - Grant ID: DE180100203

Funded Activity Summary

Deep space-time models for modelling complex environmental phenomena. This project aims to adapt deep-learning models, used in areas of artificial intelligence such as image tagging and automatic text translation, to improve our understanding of the environment. The project expects to develop new theory for deep-learning models to learn from measurement data and numerical-model output about environmental phenomena that evolve in space and time, such as ice sheets and the atmosphere. Expected outcomes include the ability to provide reliable predictions and quantification of uncertainty on environmental concerns of national importance, such as sea-level rise. Key benefits include improved risk management and mitigation, for example through financial incentives or infrastructure planning.

Funded Activity Details

Start Date: 04-06-2018

End Date: 31-12-2023

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $348,575.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Applied Statistics | Statistics | Knowledge Representation and Machine Learning

ANZSRC Socio-Economic Objective (SEO)

Expanding Knowledge in the Earth Sciences | Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Expanding Knowledge in the Mathematical Sciences |

Other Keywords

Applied Statistics | Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Expanding Knowledge in the Earth Sciences | Expanding Knowledge in the Mathematical Sciences | Knowledge Representation and Machine Learning | Statistics

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