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Our group is moving to the University of Chicago. We have a number of PhD/postdoc positions for Spring/Fall 2025 to work at the intersection of extreme weather, scientific machine learning, climate change, and computational physics. See Available Positions for more details. In particular, see the new opening for postdocs and research scientists to work on AI weather forecasting across scales.

We study extreme weather, climate change, geophysical turbulence, and scientific machine learning (ML) through the lens of multi-scale nonlinear dynamics. We aim to integrate tools and concepts from fluid and climate dynamics, applied and computational math, and ML to gain a deeper theoretical understanding of these phenomena and develop better computational tools to predict them. Examples of problems of interest are blocking events, heat waves and hurricanes, subgrid-scale modeling of geophysical turbulence and other processes,  and explainable physics-informed ML. Our research has been supported by NSF, ONR, NASA, Schmidt Sciences, National Academy of Sciences, C3.ai Digital Transformation Institute, Accenture, Microsoft AI, and Mitsubishi Electric Research Lab.

Recent News:

  • September 2024: Prof. Hassanzadeh is the director of the new AI for Climate (AICE) Initiative at UChicago’s Data Science Institute. AICE aims at interdisciplinary integration of AI with fundamental domain knowledge to accelerate and transform climate research with a focus on both scientific advances and societal impacts.
  • August 2024: Congratulations to PhD student Karan Jakhar for receiving three honors for his work on equation discovery of turbulence closure using ML: AGU Editor’s Highlight for his new paper in JAMES, 2023 AGU Outstanding Student Presentation Award (OSPA), and best presentation award at Schmidt Sicneces Cross-VESRI meeting in Cambridge University.
  • October 2023: Check out the recording of the talk titled “Integrating physics, data and scientific machine learning to predict climate variability and extremes“, which was gaving as a part of the APS-GPC seminar series.

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