Deep Learning Earth System Modeling Evaluation - Postdoctoral Researcher

Position: Deep Learning for Earth System Modeling Evaluation - Postdoctoral Researcher
Company Description

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

Job Description

We have an opening for a Postdoctoral Researcher in Deep Learning for Earth System Modeling who will conduct cutting edge research at the intersection of deep learning, atmospheric science, and statistical methods to advance the evaluation and testing of AI-based Earth System models. In this position, you will be responsible for operationalizing to AI-based weather and climate models and rigorously evaluating their performance against observations and traditional models.

You will collaborate with a multidisciplinary team of experts in machine learning, atmospheric science, Earth System modelling, and model performance assessment.

This position is in the Climate Sensitivity and Impacts Group within the Atmospheric, Earth, and Energy Division.

Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.

This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years.

In this role you will

* 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.

Qualifications

* 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, Tensor Flow).

* 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.

Qualifications We Desire

* 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, Neural

GCM, ACE2, Gen Cast, Weather Next 2, is a plus.

Pay Range

$123,048 Annually

This is the lowest to highest salary we in good faith believe we would pay for this role at the…