Job Reference: 590_24_ES_AC_R3
Position: Researcher - Enhancing atmospheric chemistry modeling capabilities with machine learning and other cutting-edge technologies (R3) - AI4S
Closing Date: Tuesday, 03 September, 2024
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. BSC was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads large-scale investments and HPC provision in Europe. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress. BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 1000 staff from 60 countries.
Context And Mission: The AC group develops and maintains its own forecast model MONARCH, which is a coupled meteorological-chemistry model that can be run at global and regional scales to simulate gaseous chemistry and aerosols plus their feedbacks on radiation. It provides operational air quality forecasts over Europe within the Copernicus Atmosphere Monitoring Services (CAMS) and dust forecasts over Africa, Middle-East and Europe in the framework of the Barcelona Dust Regional Center (BDRC). Data assimilation is also used by the AC group to compute retrospectives analyses of the atmospheric composition or to estimate aerosol emissions. The AC group maintains a comprehensive storage system of Earth system observations (in-situ and satellite) for model evaluation, process analysis and data assimilation. Data curation of the storage system is achieved through harmonization and new standardized processing methodologies.
Key Duties: Propose and implement a strategy for integrating one or several of these cutting-edge technologies to improve our capabilities for modeling the atmosphere's chemical composition. Review the existing literature on the field and related disciplines. Contribute in the preparation of large-scale atmospheric composition training datasets for machine learning developments. Participate in collaborative projects with partner institutions. Conduct original research and dissemination in high impact peer-reviewed publications, internal and external meetings and conferences. Requirements: Education: A PhD degree in atmospheric chemistry, physics, climate, remote sensing, environmental engineering, data science, machine learning, computer science or similar.
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