Deep Learning for Earth System Modeling Evaluation - Postdoctoral Researcher

Lawrence Livermore National Laboratory (LLNL) is seeking a Postdoctoral Researcher in Deep Learning for Earth System Modeling. The role involves conducting research on AI-based Earth System models and collaborating with a multidisciplinary team to evaluate their performance against traditional models and observational data.

Responsibilities

• Conduct research on the ability of Deep Learning Earth System Models (DL-ESMs) to accelerate Earth System science
• Apply a set of standard metrics based on DL-ESM outputs, and design, develop and carry out innovative advanced experiments (e.g., storyline analyses, or implementing nudging methods) to evaluate the trustworthiness of DL-ESMs against conventional ESMs and observational datasets
• Engage and actively contribute to the international initiative AI-MIP, an effort to define a standard set of experiments for evaluating and benchmarking state-of-the-art DL-ESMs
• Pursue independent research and work closely with colleagues in a multidisciplinary team environment to advance research goals
• Prepare comprehensive documentations of findings to guide future users
• Publish research results in peer-reviewed scientific or technical journals and present results at external conferences and seminars
• Travel as required to coordinate research with collaborators or participate in relevant hackathons
• Perform other duties as assigned

Skills

• PhD in Atmospheric Science, Data Science, or related field
• Experience conducting research in atmospheric science or closely related fields
• Ability to manipulate and analyze large, and complex ESM output datasets, such as those collected in the Coupled Model Intercomparison Project
• Proficient programming skills using Python and demonstrated experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
• Experience using high-performance computing environments
• Proficient verbal and written communication skills as evidenced by peer reviewed publications and presentations
• Ability to work independently as well as effectively in a collaborative, multidisciplinary team environment
• Ability to travel as required
• Experience developing and applying advanced statistical algorithms or machine learning models for one or more of the following applications: weather forecasting, subseasonal-to-seasonal (S2S) prediction, storyline analysis, nudging, green function, or dynamical adjustment
• Familiarity with the analysis of weather extremes, variability across time scales, or the impact of extreme events on infrastructure, natural, or human systems
• Experience with one AI-based weather prediction model, for example, NeuralGCM, ACE2, GenCast, WeatherNext 2, is a plus

Benefits

• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)

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