PhD student in Earth Science

Are you our new colleague?

Are you an ambitious and highly motivated candidate with a strong interest in scientific data analysis for climate research? Then you are the person we are looking for.

SRON is the space research expert institute in the Netherlands, developing and using Earth observation satellite instruments for climate and air quality research. SRON is the (co-) Principal Investigator institute for a.o. the Dutch satellite instruments TROPOMI, SPEX-One, and TANGO. We work together with international partners such as the UN, ECMWF CAMS, Global Methane Hub, and the World Bank to turn these data into emission reductions.

Your work at SRON will improve the detection, monitoring, and understanding of large methane emissions from (point) sources worldwide. You will make use of satellite observations, in particular from the recently launched Japanese GOSAT GW – TANSO-3 instrument. For this SRON collaborates with Japanese colleagues including from JAXA and NIES.

You will be part of the methane research team in the Earth Science Group (ESG) of SRON. The ESG consists of approximately 40 scientists (permanent, postdocs, PhD students) that work on the data processing and interpretation of satellite measurements as well as the definition of new instrumentation and guiding the development. Our team develops methods to detect and identify sources and quantify emissions of various trace gases using satellite data in conjunction with atmospheric modelling and machine learning.

Our research aims to address topics with societal relevance.

Methane is responsible for about a third of global warming, and reducing methane emissions is the most effective way to slow down climate change on the short term. A large fraction of total methane emissions come from a limited number of super-emitting (point) sources. Within this project, you will expand our capabilities to detect, quantify, and analyze methane emissions from (point) sources using the recently launched GOSAT-GW TANSO-3 instrument.

GOSAT-GW provides an exciting new view on global methane, including through a “zoom-in” mode that can capture methane concentrations at up to 1-km resolution. As a start, we will start exploring and utilizing these data by further developing and adapting our machine learning based plume detection algorithms originally designed for TROPOMI. We will look at both the global scale for the detection of super-emitters as well as the more local scale (e.g. urban, oil and gas fields etc.).

The work will be done as part of our contribution to the UN’s International Methane Emission Observatory (IMEO) developed Methane Alert Response System (MARS) with the aim to support companies and governments to meet their international agreements such as the Global methane Pledge and the Paris agreement. Your work will fit in and complement our on-going research in the group on methane (super-emitters) to support of mitigation activities.

You will also enroll in the SENSE Research School at the Vrije Universiteit in Amsterdam as part of your PhD program.

We are looking for an ambitious, highly motivated, and research-focused student. The qualified applicant should have an M.Sc. degree in (atmospheric) physics or a similar field with strong experience in programming. Experience with the application/use of machine learning and/or experience with atmospheric observations would be an asset. Good English language skills (both oral and written) and the ability to both work in a team and independently are expected.

The position we offer at SRON is a full-time position in which you will be employed by NWO- I (The Netherlands Organization for Scientific Research Institutes) and will initially be for a period of 18 months. This will be extended to 4 years when your performance evaluation after 9 months is satisfactory. The salary will be in accordance with the salary scales of NWO-I, starting at €3.115,- in the first year and rising to €3.989,- in the fourth year of employment.

Compensation for commuting expenses will be provided.

NWO has good secondary employment conditions…