Machine Learning Earth Science Postdoctoral Research Associate

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.

• 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 Num Py/Sci Py 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.

• 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.

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.

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

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

• PPO or high‑deductible medical insurance with a nationwide…