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# Post-Doc in Atmospheric Science and Machine Learning

- **Company:** Sron. The Esg
- **Location:** Leiden, Netherlands
- **Salary:** €3,802.0 - €5,758.0
- **Contract:** Temporary contract
- **Skills:** Computer Programming, Machine Learning, Deep Learning, Data Analytics
- **Published:** July 11, 2026
- **Apply:** https://www.academictransfer.com/nl/jobs/362532/post-doc-in-atmospheric-science-and-machine-learning/apply/

## About the Role

Are you an ambitious, highly motivated, and result driven (postdoctoral) scientist with experience in interpreting atmospheric (satellite) observations and machine learning? Then you are the person we are looking for., We are looking for an ambitious, highly motivated, and result driven scientist with a PhD in atmospheric sciences or a similar degree, with experience in the interpretation of atmospheric (e.g. satellite, aircraft etc.) observations and machine learning applications. Strong programming and data analytics skills are also expected. Experience with research on atmospheric CO, transport modelling and/or flux inversions is considered an asset. A highly developed proficiency in written and oral English is essential, and the candidate should be capable of working both independently and in a team.

## Description

Within the COGNITO project, you will apply novel machine learning approaches to detect CO plumes in global satellite observations from TROPOMI and possibly complemented by Sentinel-5. Building on successful methodologies previously developed for methane super-emitter detection, you will create a global database of CO emission events and investigate the emission source rates from hundreds of industrial facilities and urban regions worldwide.

Your work will include:

 * Apply existing machine learning algorithms for automated detection of CO plumes in satellite observations
 * Building a global catalogue of CO emission events from 2018 onwards
 * Quantifying emissions from cities and iron and steel production facilities using inhouse quantification tools
 * Evaluating temporal variability in emissions, including seasonal cycles, operational changes, and potential signatures of industrial decarbonization efforts
 * Assessment of emission quantification tools using atmospheric transport modeling
 * Comparing satellite derived emissions with bottom up inventories and reporting systems
 * Publishing results in leading international scientific journals and presenting your work at conferences and stakeholder meetings

The project offers a unique opportunity to work at the intersection of atmospheric science, machine learning, satellite remote sensing, and climate policy.


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