Machine Learning for Earth Science Postdoctoral Research Associate

About the role

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.

Responsibilities

• 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.
• 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.
• 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.

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.

Qualifications

• 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.
• 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.

Skills

• Strong programming skills in Python, R, or equivalent languages.
• Experience with machine learning frameworks like TensorFlow, PyTorch, or JAX.
• Knowledge of scientific computing libraries such as NumPy, SciPy, and Pandas.
• Understanding of Earth system science concepts and models.
• Experience with data assimilation techniques.
• Ability to work in a collaborative, interdisciplinary environment.
• Good communication skills, both written and oral.

Benefits

• Competitive salary commensurate with experience.
• Flexible work schedule.
• Access to a comprehensive benefits package including health, dental, and vision insurance, retirement plans, and paid time off.
• Opportunities for professional development and career growth.
• Collaborative and interdisciplinary research environment.

Pay

Salary is competitive and commensurate with experience.

Schedule

The position is full-time and is expected to be performed primarily onsite at Los Alamos National Laboratory in Los Alamos, NM.