Zavud Baghirov

Zavud Baghirov is a Doctoral Researcher at the Max Planck Institute for Biogeochemistry, where he is a PhD candidate in the Global Diagnostic Modelling research group under the International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC). His work focuses on hybrid modeling approaches that integrate deep learning with physical models to understand global water and carbon cycles.

His educational background includes:

• MSc in Environmental Sciences (specializing in Environmental Remote Sensing and Modelling) from the University of Trier (2017-2021)
• BSc in Ecology from Baku State University (2013-2017)

Baghirov's research interests center on hybrid modeling, which combines deep learning with physical models using Earth observation data. His current work aims to develop global models of coupled terrestrial water and carbon cycles while quantifying uncertainties. This approach bridges the gap between data-driven machine learning and physics-based modeling, offering more interpretable and robust predictions for Earth system science.

His recent publications demonstrate a strong trend in applying hybrid deep learning techniques to model global biogeochemical cycles, particularly focusing on water and carbon dynamics. The research integrates atmospheric and land observations to constrain models, enhancing their physical consistency and predictive capability.

As a doctoral researcher, Baghirov is actively involved in the Global Diagnostic Modelling research group. He collaborates within the International Max Planck Research School for Global Biogeochemical Cycles, contributing to a vibrant community of early-career scientists focused on understanding Earth's biogeochemical processes.