Job Title Predoctoral student (PhD) to develop AI-based pollutant emission inverse modelling with Earth Observation (R1) Location Barcelona, Catalonia, Spain Contract Type /

Working Hours
Full-time contract (35h/week), Open-ended contract due to technical and scientific activities linked to the project and budget duration Closing Date Monday, 12 October, 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, 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. Look at the BSC

experience:
BSC-CNS YouTube Channel Let's stay connected with BSC Folks We are particularly interested for this role in the strengths and lived experiences of women and underrepresented groups to help us avoid perpetuating biases and oversights in science and IT research. We promote Equity, Diversity and Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences. Context And Mission We are looking for an atmospheric data scientist / modeler to join the Atmospheric Composition group within the Earth Sciences department at the BSC-CNS. Composed of about 50 members (research engineers, predocs,



postdocs, senior scientists), the AC group aims at better understanding and predicting the spatiotemporal variations of atmospheric pollutants along with their effects upon air quality, weather and climate. This is addressed through the continuous development and application of numerical models over multiple scales, from weather to climate and from integral to urban scales. The AC group is the research backbone of the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH), a cutting-edge atmospheric composition model used for both research and operational activities, that contains advanced chemistry and aerosol packages coupled online with a meteorological driver. MONARCH is part of the ensemble Copernicus Atmospheric Monitoring Service (CAMS) regional air quality forecasting system that provides operational forecast and analysis over Europe. CAMS is a key component of the European Union’s Earth observation system. MONARCH also runs operationally at the first World Meteorological Organization (WMO) Barcelona Dust Regional Center (BDRC) for Northern Africa, the Middle East and Europe, and the International Cooperative for Aerosol Prediction (ICAP) ensemble of global aerosol forecasts. The group also develops the HERMES emission model, which provides high-resolution anthropogenic emissions for Spain and Europe.



Anthropogenic emission inventories remain one of the largest sources of uncertainty in air quality modelling: they are typically compiled with a delay of several years, rely on activity data and emission factors with large uncertainties, and poorly capture rapid changes in human activities. At the same time, the current and upcoming generation of Earth Observation (EO) missions – such as TROPOMI on Sentinel-5P or the geostationary Sentinel-4 – provide an unprecedented view of atmospheric pollutants at high spatial and temporal resolution. Exploiting these observations to constrain emissions through traditional inverse modelling approaches is, however, computationally very demanding. To prepare the next generation of emission estimates supporting research
- and policy-relevant applications, we are seeking a highly motivated predoctoral student (PhD, R1) to develop AI-based inverse modelling methods able to infer pollutant emissions (with a first focus on NOx) from satellite observations and MONARCH simulations. He/she will explore, adapt and improve cutting-edge deep learning approaches. Particular attention will be paid to the methodological aspects, including how to properly deal with the intrinsic gaps of EO data and the impact of the averaging kernels. This research will start focusing on the Iberian Peninsula but should then scale to European scale, and to additional species. The successful applicant will be part of an active group of researchers focusing on leveraging deep learning technologies to address key scientific and policy-oriented challenges (currently about 7 people in the AC group, about 15-20 in the BSC Earth Sciences department). To conduct this research, he/she will have access to the groundbreaking High-Performance Computing infrastructure of BSC, notably MareNostrum

📌 Predoctoral student (Barcelona)
🏢 Barcelona Supercomputing Center (Bsc)
📍 Barcelona