Overview
Job Reference: 256_26_ES_AC_R2
Position: Postdoctoral position on Data assimilation for the atmospheric composition (R2)
Closing Date: Wednesday, 03 June, 2026
About BSC
The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, and is the hosting entity for EuroHPC JU. The mission of BSC is to research, develop and manage information technologies to facilitate scientific progress. BSC combines HPC service provision and R&D; into computer and computational science across life, earth and engineering sciences with over 1000 staff from 60 countries.
Context And Mission
We are looking for an enthusiastic researcher to join the Atmospheric Composition (AC) group within the Earth Sciences department. The AC group aims to understand and predict spatiotemporal variations of atmospheric pollutants and their effects on air quality, weather and climate. This is addressed through the development and application of numerical models from weather to climate and from integral to urban scales. The group develops MONARCH, a coupled meteorological-chemistry model used for global and regional forecasts, data assimilation with LETKF, and retrospective analyses of atmospheric composition and dust emissions.
To address limitations in the current LETKF implementation and extend the control vector and assimilated observations, the AC group offers a postdoctoral position to develop, test, evaluate, and conduct research on methodological aspects of the new implementation for the atmospheric composition state, parameters and emission estimation problems. Successful candidates will design and perform experiments to compare the new implementation with the current assimilation system. The position involves collaboration with atmospheric and computer science engineers and researchers.
Key Duties
- Collaborate in implementing state-of-the-art ensemble-variational schemes on the BSC’s HPC and adapt them to the MONARCH workflow.
- Conduct data assimilation experiments with both synthetic and real observations of aerosols and gases, in analysis and forecast mode.
- Evaluate results, perform sensitivity tests and identify potential improvements to the DA schemes.
- Conduct original research and dissemination in peer reviewed publications, internal and external meetings and conferences.
Requirements
Education
- A doctoral degree in atmospheric sciences, environmental sciences, applied mathematics, physics, engineering, or related disciplines.
Essential Knowledge and Professional Experience
- Experience with data assimilation and/or inverse methods in geosciences.
- Experience with computationally demanding models (e.g., climate, atmosphere or ocean).
- Experience programming in Fortran, C++ or both.
- Experience working in Unix/Linux environments.
Additional Knowledge and Professional Experience
- Experience with numerical weather prediction or atmospheric composition models.
- Demonstrated scientific expertise, including peer reviewed publications.
- Experience with variational data assimilation.
- Experience with Python.
Competences
- Very good interpersonal skills.
- Excellent written and verbal communication skills.
- Ability to take initiative, prioritize and work under set deadlines.
- Ability to work both independently and within a team.
Conditions
- The position will be located at BSC within the Earth Sciences Department.
- Full-time contract (35h/week), flexible working hours, extensive training plan, restaurant tickets, private health insurance, relocation support.
- Duration: Open-ended contract tied to project and budget duration.
- Holidays: 22 days + 6 personal days + 24th and 31st of December per collective agreement.
- Salary: Competitive salary commensurate with qualifications and experience.
- Starting date: July 2026.
Notes
Any information not directly related to the responsibilities, qualifications, or benefits has been removed to meet formatting quality standards. This description retains the core job responsibilities and requirements.
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