Machine Learning for Earth Science Postdoctoral Research Associate

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

• 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., * 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.

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. 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.