
How to apply
Apply for a joint PhD with the Melbourne CNRS Network.
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The land and ocean remove half the carbon dioxide (CO2) we add to the atmosphere each year, greatly reducing the severity of climate change. However, as climate itself changes this uptake, especially the land component, is at risk.
To understand this risk we must observe how CO2 uptake changes with time, understand the key processes and embed that understanding in predictive models.
This project will apply machine learning and two state-of-the-art streams of new satellite data to map recent patterns of CO2 uptake by the land and project this improved knowledge into prediction.
The project will be carried out in parallel between the Laboratory for the Science of Climate and Environment (LSCE) near Paris and the University of Melbourne (UoM) using two world-leading models and contributing to an open-source data analysis system to which others will be invited to contribute.
The LSCE project will focus primarily on the development of the assimilation of atmospheric satellite CO2 data through the use of a Machine Learning based approach; while the UoM based project will focus more on the assimilation of Solar Induced Fluorescence (SIF) from terrestrial vegetation.
Both teams will use independent land surface models and transport models to characterise the impact of model structural errors on land carbon budgets.
The University of Melbourne: Professor Peter Rayner
CNRS, Laboratory for the Science of Climate and Environment: Professor Philippe Peylin
*Click on the researcher's name above to learn more about their publication and grant successes.
We are seeking a PhD candidate with the following skills:
To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply.
Apply for a joint PhD with the Melbourne CNRS Network.
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