[ES_JOBS_NET] Postdoctoral Appointee - AI Foundation Models for Atmospheric Science, Argonne National Lab

[Christine Wiedinmyer]

https://argonne.wd1.myworkdayjobs.com/en-US/Argonne_Careers/job/Lemont-IL-USA/Postdoctoral-Appointee---AI-Foundation-Models-for-Atmospheric-Science_421783

Postdoctoral Appointee - AI Foundation Models for Atmospheric Science

Argonne National Laboratory, a U.S. Department of Energy national
laboratory located near Chicago, Illinois, has an opening for a highly
motivated postdoctoral appointee in the Decision and Infrastructure
Sciences Division.

Machine learning (ML), specifically deep learning (DL), has been
demonstrated to successfully predict the weather for 1-14 days with skill
on par with numerical weather prediction at a fraction of the computational
cost. Recently Argonne successfully implemented, AERIS, a state-of-the-art
seasonal-to-subseasonal (S2S) weather model AI model. A successful
candidate will collaborate with this group to evaluate AERIS at S2S scales,
couple ocean component to the model, data assimilation and regional
refinement.  In particular, this position will utilize generative AI to
create a calibrated ensemble system for S2S at high resolution (30-km) to
deliver probabilistic weather forecasts beyond 14 days to allow for
actionable, local-scale impacts on infrastructure and communities.

The ideal candidate would be a PhD in geophysical sciences, computer
science, or machine learning with experience in developing and verifying
deep learning-based models for large dynamical systems (e.g. weather).
Expertise in data and model parallelisms for distributed training on large
GPU-based machines is essential. Candidates with experience using
diffusion-based or other generative AI methods as well as experience in
atmospheric science, especially weather modeling, are particularly sought
after. This is a one-year position that can be extended to two years that
we want to fill immediately.

*Responsibilities:*

Contributes technical expertise through analysis and support for programs
and projects associated with machine learning, HPC, and computational
problems related to earth system science and other dynamical systems.
Development, evaluation, and applying machine learning/computational
approaches, synthesis activities, computational tools, compiling results,
preparing reports, publications, and documentation.  In particular, this
position is for projects related to applying and developing machine
learning-based weather models for the S2S timeframe with an emphasis on
generative AI techniques, evaluating such models, and working with a team
of scientists interested in pushing the boundary of predictability.

Position Requirements

   -

   Recent or soon-to-be-completed PhD (completed within the last 0-5 years)
   in geophysical sciences, computer science, or machine learning with 0 to 2
   years of experience
   -

   Knowledge of deep learning, PyTorch/JAX, and scaling deep learning
   models to large GPU-based machines
   -

   Technical knowledge in using HPC systems for visualization and analysis
   -

   Technical knowledge of large, dynamical systems (preferably the
   atmosphere)
   -

   Knowledge and experience in writing scientific code
   -

   Skills in clear, concise writing of technical papers, and interacting
   and communicating effectively with colleagues
   -

   Problem solving skills
   -

   Organizational skills and flexibility in coordinating a broad spectrum
   of activities
   -

   Knowledge of atmospheric dynamics, process scale models, and numerical
   computation techniques
   -

   Knowledge of data analysis
   -

   Knowledge of using atmospheric observational datasets, data assimilation
   techniques, and statistics
   -

   Familiarity subseasonal-to-seasonal modeling and or coupled
   atmosphere-ocean modeling
   -

   Ability to work and communicate with stakeholders from public and
   private sectors
   -

   A successful candidate must have the ability to model Argonne’s Core
   Values: Impact, Safety, Respect, Integrity, and Teamwork.

Job Family
Postdoctoral

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

The expected hiring range for this position is $72,879.00-$121,465.00.

Please note that the pay range information is a general guideline only. The
pay offered to a selected candidate will be determined based on factors
such as, but not limited to, the scope and responsibilities of the
position, the qualifications of the selected candidate, business
considerations, internal equity, and external market pay for comparable
jobs. Additionally, comprehensive benefits are part of the total rewards
package.

Click *here* <https://www.anl.gov/hr/healthcare-insurance> to view Argonne
employee benefits!

*As an equal employment opportunity employer, and in accordance with our
core values of impact, safety, respect, integrity and teamwork, Argonne
National Laboratory is committed to a safe and welcoming workplace that
fosters collaborative scientific discovery and innovation. Argonne
encourages everyone to apply for employment. Argonne is committed to
nondiscrimination and considers all qualified applicants for employment
without regard to any characteristic protected by law.*

*Argonne employees, and certain guest researchers and contractors, are
subject to particular restrictions related to participation in Foreign
Government Sponsored or Affiliated Activities, as defined and detailed in
United States Department of Energy Order 486.1A. You will be asked to
disclose any such participation in the application phase for review by
Argonne's Legal Department.  *

*All Argonne offers of employment are contingent upon a background check
that includes an assessment of criminal conviction history conducted on an
individualized and case-by-case basis.  Please be advised that Argonne
positions require upon hire (or may require in the future) for the
individual be to obtain a government access authorization that involves
additional background check requirements.  Failure to obtain or maintain
such government access authorization could result in the withdrawal of a
job offer or future termination of employment.*
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