Postdoctoral Researcher in Deep Learning for Earth System Modeling Evaluation

Lawrence Livermore National Laboratory, in collaboration with the University of Washington, is seeking a Postdoctoral Researcher specializing in Deep Learning for Earth System Modeling Evaluation. This unique opportunity is led by Giuliana Pallotta, a Group Leader and Scientist in Applied Machine Learning for National Security, with expertise in anomaly detection and Bayesian statistics. The position is based in the San Francisco Bay Area. The successful candidate will work at the intersection of deep learning, atmospheric science, and statistical methods. The primary focus is on operationalizing and rigorously evaluating AI-based weather and Deep Learning Earth System models, comparing them against observations and conventional models. Research areas include, but are not limited to, subseasonal-to-seasonal (S2S) prediction, storyline analysis, nudging, Green’s function approaches, and dynamical adjustment. Applicants should have a PhD in computer science, atmospheric science, statistics, or a related field, with strong expertise in deep learning, AI-based modeling, and statistical methods. Experience with Earth system modeling and advanced data analysis techniques is highly desirable. The position offers the chance to contribute to cutting-edge research in AI-driven weather and climate modeling, working alongside leading experts in the field. While specific funding details are not provided, postdoctoral positions at national laboratories are typically fully funded and include salary and benefits. Interested candidates are encouraged to apply or share the opportunity via the provided LinkedIn post for further details and application instructions.