Machine Learning for Earth Science Postdoctoral Research Associate

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 60,000 - 85,000; Full time

Benefits offered by this job

Medical insurance

Dental and vision insurance

Tuition assistance

401(k) matching

Flexible schedules

Relocation assistance

Job summary

Los Alamos National Laboratory is seeking a postdoctoral candidate skilled in machine learning and scientific computing to join a multidisciplinary team. The role focuses on developing innovative methods for Earth system science, integrating various models and simulations.

Candidates should possess a PhD in a related field, strong programming skills in Python, and experience with ML libraries. A Q security clearance is required.

Qualifications

• Experience in machine learning, scientific computing, or data‑driven modeling.
• Strong mathematical training in probability and statistics.
• Excellent scientific programming skills with modern ML libraries.

Responsibilities

• Develop machine learning capabilities for Earth system science applications.
• Collaborate with scientists on mission-relevant problems.
• Implement empirical validation and methodology development.

Skills

Machine learning

Scientific computing

Statistical methods

Scientific programming

Education

PhD in a relevant field

Tools

Python

PyTorch

JAX

Job description

What You Will Do

The Computational Physics and Methods group (CAI‑2) is seeking 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, with core activities including 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.

Minimum Job Requirements

• Experience in machine learning, scientific computing, data‑driven modeling, or statistical methods for complex physical systems, evidenced by 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 Earth‑science machine learning areas such as surrogate modeling, emulation, data assimilation, uncertainty quantification, probabilistic prediction, causal inference, downscaling, or multi‑modal data integration.
• Excellent scientific programming skills with hands‑on experience using modern ML libraries and tools (e.g., PyTorch, JAX) and high‑level languages such as Python, including 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.

Desired Qualifications

• Experience developing or applying advanced scientific machine learning methods for complex physical systems, including 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.
• 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.

Education and 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.

Location

Onsite in Los Alamos, NM. The work location is at Los Alamos National Laboratory.

Security Clearance

Q clearance required. United States citizenship or lawful permanent residency is required.

Benefits

• PPO or high‑deductible medical insurance with a nationwide network
• Dental and vision insurance
• Free basic life and disability insurance
• Paid childbirth and parental leave
• 401(k) with 6% matching plus 3.5% annual contributions
• Learning opportunities and tuition assistance
• Flexible schedules and time off (PTO and holidays)
• Onsite gyms and wellness programs
• Relocation assistance for moves outside a 50‑mile radius

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 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.