Post-Doc in Atmospheric Science and Machine Learning

Sron. The Esg
Leiden, Netherlands
3 days ago
Apply on www.academictransfer.com
Prepare application

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Compensation
€3,802.0 - €5,758.0
Working hours
Regular working hours
Languages
English

Tech stack

Computer Programming Machine Learning Data Analytics

Job 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.

Requirements

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.

Benefits & conditions

The position we offer at SRON is full-time for a period of two years with the possibility of a two-year extension in which you will be employed by NWO-I, The Netherlands Organization for Scientific Research Institutes. The salary will be in accordance with NWO salary scale 10, commensurate with your education and experience, be for a maximum of €5.758,- gross per month on a full-time basis.

NWO has excellent secondary employment conditions such as:

  • An end-of-year bonus of 8,33% of the gross yearly salary;
  • A holiday allowance of 8% of the gross yearly salary;
  • 42 days of vacation leave a year on a full-time basis;
  • An excellent pension scheme;
  • Options for (additional) personal development;
  • Excellent facilities for parental leave;
  • Ample training opportunities;
  • Possibility of flexible working hours and hybrid working

About the company

You will contribute to the newly funded COGNITO project (carbon monoxide (CO) Global aNalysis, source Identification and emission quantification using TROPOMI Observations). COGNITO aims to develop the first global, satellite-based system for detecting and quantifying carbon monoxide emissions from major urban areas and industrial facilities, with a particular focus on the iron and steel sector. By using TROPOMI observations with advanced machine learning techniques, the project will provide independent information on emission patterns and support efforts to improve emission inventories and evaluate decarbonization strategies worldwide.

Your team

You will become part of the Earth Science Group (ESG) at SRON. The ESG consists of approximately 40 scientists, postdoctoral researchers, and PhD students working on the interpretation of satellite observations, atmospheric modelling, data science, and the development of future Earth observation missions. You will join a research team specializing in the detection and quantification of atmospheric emissions using satellite observations, atmospheric transport modeling, and machine learning. The team has pioneered the use of satellite observations for identifying methane super-emitters and quantifying emissions from industrial and urban sources worldwide., SRON is the Space Research Organisation Netherlands. Based at locations in Leiden (applies for this position) and Groningen, SRON combines fundamental scientific research, technology innovation, and instrument development to enable breakthroughs in astrophysics, exoplanet research, and earth atmospheric research from space. In partnership with leading international partners and space agencies such as ESA, NASA, and JAXA, SRON contributes science and technology to international space missions as well as expertise and support to the Dutch and international scientific communities.

SRON is the national base for the Netherlands participation in the ESA science program. On the national level, it works in close collaboration with the Dutch universities, other NWO institutes, and governmental agencies to pursue a cohesive agenda for space-based research.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.academictransfer.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:32 min

Structuring platforms for new services and data analytics

Nevelina Aleksandrova · LIVE

2:36 min

Applying supervised machine learning for practical rule extraction

Katja Träumner

3:31 min

Revolutionizing computer programming through natural language code generation

Demetris Cheatham Demetris Cheatham +1 · World Congress 2024

3:53 min

Accessing large environmental datasets to analyze global climate challenges

Sohan Maheshwar · LIVE

1:10 min

Introduction to Microsoft Fabric and data agents

Dr. Alexander Wachtel Dr. Alexander Wachtel +1 · World Congress 2025

1:17 min

Transitioning from academic physics to data science leadership

Katja Träumner

Videos

See all

Related articles

See all