Machine Learning for Earth Science Postdoctoral Research Associate

Los Alamos National Security LLC; Los Alamos (NM); On-site; USD 70,000 - 90,000; Full time

Benefits offered by this job

PPO or High Deductible medical insurance

Dental and vision insurance

Free basic life and disability insurance

Paid childbirth and parental leave

401(k) matching

Tuition assistance

Flexible schedules and PTO

Onsite gyms and wellness programs

Extensive relocation packages

Job summary

Los Alamos National Security LLC is seeking a postdoctoral candidate to join their Computational Physics and Methods group in Los Alamos, NM. The role focuses on developing machine learning capabilities to address complex Earth system science challenges. Candidates should possess a PhD in relevant fields and experience in machine learning, scientific computing, and data-driven modeling.

The position offers opportunities for interdisciplinary collaboration and applications in oceanography, hydrology, and more. Strong programming skills and a proactive research mindset are essential.

Qualifications

• Experience in machine learning and scientific computing with publications.
• Strong computational training in relevant fields like statistics and numerical analysis.
• Understanding of machine learning in Earth science applications.
• Excellent programming skills using modern ML libraries.
• Ability to work independently and in teams, communicating results clearly.
• Interest in developing new research directions.

Responsibilities

• Develop machine learning capabilities for integrating heterogeneous models.
• Collaborate with domain scientists on complex Earth science problems.
• Emphasize AI/ML methods that connect models and datasets.
• Engage in method development and empirical validation.
• Participate in cross-disciplinary collaboration and workshops.

Skills

Machine learning

Scientific computing

Data-driven modeling

Statistical methods

Scientific programming

Education

PhD in Earth System Science or related field

Tools

PyTorch

JAX

Python

NumPy/SciPy

Job description

What You Will Do

The Computational Physics and Methods group (CAI-2) seeks an outstanding postdoctoral candidate 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 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, and opportunities for cross‑disciplinary collaboration, scientific workshop organization, and conference participation.

Minimum Job Requirements

• Experience in machine learning, scientific computing, data‑driven modelling, or statistical methods for complex physical systems, 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, optimisation, machine learning theory, uncertainty quantification, or dynamical systems.
• Fundamental understanding of one or more areas relevant to Earth science machine learning, such as surrogate modelling, emulation, data assimilation, uncertainty quantification, probabilistic prediction, causal inference, downscaling, or multi‑modal data integration.
• Excellent scientific programming skills with hands‑on experience beyond online courses, using modern ML libraries and tools such as PyTorch or JAX, along with high‑level languages like Python, NumPy/SciPy, and standard scientific software practices.
• Ability to work 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 five years or to be completed soon.

Desired Qualifications

• Experience developing or applying advanced scientific machine learning methods for complex physical systems, including probabilistic modelling and uncertainty quantification, data assimilation or state estimation, inverse problems, downscaling or multi‑resolution modelling, causal modelling 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 in Los Alamos, NM. All work locations are at the discretion of management.

Benefits

• 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)

Clearance

Position will require Q clearance. Selected applicants will undergo a background investigation conducted by or on behalf of the Federal Government and must meet eligibility requirements for access to classified material. A Q clearance requires U.S. citizenship except in extremely rare circumstances. Dual citizenship may or may not be eligible for additional access authorization.

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 origin, ancestry, religion, age, sex, gender identity, sexual orientation, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by applicable federal, state and local laws and regulations. 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 [e-mail] or call (505) 664‑6947, opt. 3.

Contact

For questions about this position, contact Derek DeSantis ([e-mail]).