[ES_JOBS_NET] Postdoctoral Researcher- Deep Learning for Earth System Modeling Evaluation - Lawrence Livermore National Laboratory (LLNL)

[Christine Wiedinmyer]

*Deep Learning for Earth System Modeling Evaluation - Postdoctoral
Researcher*
https://www.llnl.gov/join-our-team/careers/find-your-job/all/all/3743990011861051

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to
strengthen the United States’ security? Come join Lawrence Livermore
National Laboratory (LLNL) where our employees apply their expertise to
create solutions for BIG ideas that make our world a better place.

We are committed to a diverse and equitable workforce with an inclusive
culture that values and celebrates the diversity of our people, talents,
ideas, experiences, and perspectives. This is essential to innovation and
creativity for continued success of the Laboratory’s mission.
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.

Note: 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, 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.



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,
   NeuralGCM, ACE2, GenCast, WeatherNext 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 time of this posting; pay will not be below any
applicable local minimum wage.  An employee’s position within the salary
range will be based on several factors including, but not limited to,
specific competencies, relevant education, qualifications, certifications,
experience, skills, seniority, geographic location, performance, and
business or organizational needs.
Additional Information

All your information will be kept confidential according to EEO guidelines.







Position Information

This is a Postdoctoral appointment with the possibility of extension to a
maximum of three years, open to those who have been awarded a PhD at time
of hire date.

Why Lawrence Livermore National Laboratory?

   - Included in 2026 Best Places to Work by Glassdoor!
   - Flexible Benefits Package
   <https://www.llnl.gov/join-our-team/culture/benefits>
   - 401(k)
   - Relocation Assistance
   - Education Reimbursement Program
   - Flexible schedules (*depending on project needs)
   - Our values - visit https://www.llnl.gov/inclusion/our-values
   <https://www.llnl.gov/diversity/our-values>

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