[ES_JOBS_NET] Machine Learning for Earth Science Postdoctoral Research Associate, Los Alamos National Laboratory

[Christine Wiedinmyer]

https://lanl.jobs/search/jobdetails/machine-learning-for-earth-science-postdoctoral-research-associate/2ea173fe-a2a2-4278-9059-4a3289f0f672



What You Will Do
The Computational Physics and Methods group (CAI-2) is seeking an
outstanding candidate for a postdoctoral position at the intersection of
machine learning, scientific computing, uncertainty quantification, and
Earth system science.
The successful candidate will join a multidisciplinary team of
mathematicians, physicists, Earth system scientists, and machine learning
researchers advancing AI-enabled methods for complex Earth science
problems. The postdoc will develop reusable machine learning capabilities
for integrating heterogeneous models, simulations, observations, and
reanalysis products across Arctic and high-latitude science applications.
Core activities will include method development, scientific software
implementation, empirical validation, and collaboration with domain
scientists on mission-relevant problems involving predictability, risk,
attribution, and multi-scale Earth system processes.
The position will emphasize composable AI/ML methods that connect
process-based models, numerical simulations, observational datasets, and
scientific workflows. Relevant methodological areas may include data-model
fusion, surrogate modeling and emulation, probabilistic prediction,
uncertainty quantification, data assimilation and state estimation,
downscaling and upscaling, and causal modeling. The position offers
exposure to multiple application domains, including ocean, sea ice, coastal
hazards, terrestrial hydrology, permafrost, ice-sheet impacts, atmospheric
extremes, and human-system risk, as well as opportunities for
cross-disciplinary collaboration, scientific workshop organization, and
conference participation.

What You Need

Minimum Job Requirements:

   - Experience in machine learning, scientific computing, data-driven
   modeling, or statistical methods for complex physical systems, as evidenced
   through a strong scientific record of peer-reviewed publications and
   presentations.
   - Strong mathematical or computational training in relevant fields, such
   as probability and statistics, stochastic processes, numerical analysis,
   scientific computing, optimization, machine learning theory, uncertainty
   quantification, or dynamical systems.
   - Fundamental understanding of one or more areas relevant to Earth
   science machine learning, such as surrogate modeling, emulation, data
   assimilation, uncertainty quantification, probabilistic prediction, causal
   inference, downscaling, or multi-modal data integration.
   - Excellent scientific programming skills with demonstrated, hands-on
   experience beyond online courses/certifications using modern ML libraries
   and tools-e.g., PyTorch and/or JAX-along with high-level languages such as
   Python, including NumPy/SciPy, and standard scientific software practices.
   - Ability to work both independently and collaboratively in an
   interdisciplinary environment, and to communicate technical results clearly
   in writing and presentations.
   - Demonstrated creativity and interest in developing new research
   directions rather than only implementing existing methods.
   - Interest in building reusable, validated, and well-documented
   scientific ML capabilities that can support multiple Earth science
   applications.


Education/Experience: PhD in Earth System Science, Applied Mathematics,
Computational or Statistical Physics, Applied Statistics, Computer Science,
Atmospheric Science, Oceanography, Hydrology, or a related field, completed
within the last 5 years or to be completed soon.

Desired Qualifications:

   - Experience developing or applying advanced scientific machine learning
   methods for complex physical systems, including one or more of the
   following: probabilistic modeling and uncertainty quantification, data
   assimilation or state estimation, inverse problems, downscaling or
   multi-resolution modeling, causal modeling or attribution, explainable ML,
   physics-informed or structure-preserving architectures, and scalable
   analysis of large simulations, reanalysis products, remote sensing data, or
   observational datasets.
   - Prior research experience developing and/or implementing machine
   learning methods for Earth system science, hydrology, oceanography,
   atmospheric science, cryosphere science, geoscience, or another physical
   science domain.
   - Prior research experience with emulators, surrogate models, neural
   operators, reduced-order models, Gaussian processes, generative models,
   ensemble methods, or other approaches for accelerating or approximating
   expensive simulations.
   - Comfort with high-performance computing environments, including
   clusters, GPUs, job schedulers, parallel workflows, and scalable
   data-management practices.
   - Interest in scientific workflow design, provenance capture, benchmark
   construction, validation protocols, metadata standards, or reusable
   software infrastructure for interdisciplinary research.



Work Location: The work location for this position is onsite and located in
Los Alamos, NM. All work locations are at the discretion of management.

Note to Applicants:

For full consideration, please provide a comprehensive CV with
publications, a cover letter describing your qualifications and how you
meet the job requirements, and the name and contact information of at least
three professional references familiar with your work. For questions about
this position, contact Derek DeSantis (ddesantis at lanl.gov).

For more information about working at LANL, visit our career page:
 https://www.lanl.gov/careers/index.php
<https://www.lanl.gov/careers/index.php>.

Outstanding candidates may be considered for a Postdoctoral Fellowship. For
more information about LANL's Postdoc Program, go to:
 https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php
<https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php>

Due to federal restrictions contained in the current National Defense
Authorization Act, citizens of the People's Republic of China-including the
special administrative regions of Hong Kong and Macau-as well as citizens
of the Islamic Republic of Iran, the Democratic People's Republic of Korea
(North Korea), and the Russian Federation, who are not Lawful Permanent
Residents ("green card" holders) are prohibited from accessing facilities
that support the mission, functions, and operations of national security
laboratories and nuclear weapons production facilities, which includes Los
Alamos National Laboratory.
Where You Will Work

Located in Northern New Mexico, Los Alamos National Laboratory (LANL) is a
multidisciplinary research institution engaged in strategic science on
behalf of national security. LANL enhances national security by ensuring
the safety and reliability of the U.S. nuclear stockpile, developing
technologies to reduce threats from weapons of mass destruction, and
solving problems related to energy, environment, infrastructure, health,
and global security concerns. Our generous benefits package includes:

   - PPO or High Deductible medical insurance with the same large
   nationwide network
   - Dental and vision insurance
   - Free basic life and disability insurance
   - Paid childbirth and parental leave
   - Award-winning 401(k) (6% matching plus 3.5% annually)
   - Learning opportunities and tuition assistance
   - Flexible schedules and time off (PTO and holidays)
   - Onsite gyms and wellness programs
   - Extensive relocation packages (outside a 50 mile radius)

Additional Details

Directive 206.2 - Employment with Triad requires a favorable decision by
NNSA indicating employee is suitable under NNSA Supplemental Directive 206.2
<https://directives.nnsa.doe.gov/supplemental-directive/sd-0206-0002>.
Please note that this requirement applies only to citizens of the United
States. Foreign nationals are subject to a similar requirement under DOE
Order 142.3A.

Clearance: Q (Position will be cleared to this level). Selected applicants
will be subject to a background investigation conducted by or on behalf of
the Federal Government, and must meet eligibility requirements* for access
to classified matter. This position requires a Q clearance. and obtaining
such clearance requires US Citizenship except in extremely rare
circumstances. Dependent upon the position, additional authorization to
access classified information may be required, which may or may not be
available to dual citizens. Receipt of a Q clearance and additional access
authorization ultimately is a decision of the Federal Government and not of
Triad.

New-Employment Drug Test: The Laboratory requires successful applicants to
complete a new-employment drug test and maintains a substance abuse policy
that includes random drug testing. Although New Mexico and other states
have legalized the use of marijuana, use and possession of marijuana remain
illegal under federal law. A positive drug test for marijuana will result
in termination of employment, even if the use was pre-offer.

Internal Applicants: Regular appointment employees who have served the
required period of continuous service in their current position are
eligible to apply for posted jobs throughout the Laboratory. If an employee
has not served the required period of continuous service, they may only
apply for Laboratory jobs with the documented approval of their Division
Leader. Please refer to Policy Policy P701
<https://int.lanl.gov/policy/documents/P701.pdf> for applicant eligibility
requirements.

Incentive Compensation Program: For general program information refer to
the Student Programs web page:
 https://www.lanl.gov/careers/career-options/student-internships/index.php
<https://www.lanl.gov/careers/career-options/student-internships/index.php>

Equal Opportunity: Los Alamos National Laboratory is an equal opportunity
employer. All employment practices are based on qualification and merit,
without regard to protected categories such as race, color, national
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The Laboratory is also committed to making our workplace accessible to
individuals with disabilities and will provide reasonable accommodations,
upon request, for individuals to participate in the application and hiring
process. To request a disability accommodation, email applyhelp at lanl.gov or
call (505) 664-6947, opt. 3.

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