Post-Doc in Atmospheric Science and Machine Learning
Location: Leiden, Netherlands
Type: Full-time postdoc, 2 years with possible 2-year extension (NWO-I)
Salary: NWO scale 10, up to €5,758 gross/month
Are you an ambitious scientist with experience interpreting atmospheric (satellite) observations and machine learning? Join SRON's Earth Science Group on the newly funded COGNITO project (carbon monoxide Global aNalysis, source Identification and emission quantification using TROPOMI Observations).
COGNITO aims to develop the first global, satellite-based system for detecting and quantifying CO emissions from major urban areas and industrial facilities — with a focus on the iron and steel sector — using TROPOMI observations and advanced machine learning.
Your project
- Apply machine learning algorithms for automated detection of CO plumes in satellite observations
- Build a global catalogue of CO emission events from 2018 onwards
- Quantify emissions from cities and iron/steel facilities using in-house tools
- Evaluate temporal variability (seasonal cycles, operational changes, decarbonization signatures)
- Assess quantification tools using atmospheric transport modeling
- Compare satellite-derived emissions with bottom-up inventories
- Publish in leading journals and present at conferences and stakeholder meetings
Requirements
- PhD in atmospheric sciences or a similar degree
- Experience interpreting atmospheric observations (satellite, aircraft, etc.) and machine learning applications
- Strong programming and data analytics skills
- Experience with atmospheric CO, transport modelling and/or flux inversions is an asset
- Excellent written and oral English; ability to work independently and in a team
The position remains open until filled (first selection due date was 1 September 2026).
