Temporary Machine Learning Postdoc Jobs (NOW HIRING)

What You Will Do
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. 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 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.
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, as well as opportunities for cross-disciplinary collaboration, scientific workshop organization, and conference participation.
What You Need
Minimum Job 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.

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.
Desired Qualifications:

• 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.
Note to Applicants:
For full consideration, please provide a comprehensive CV with publications, a cover letter describing your qualifications and how you meet the job requirements, and the name and contact information of at least three professional references familiar with your work. For questions about this position, contact Derek DeSantis (ddesantis@lanl.gov).
For more information about working at LANL, visit our career page: https://www.lanl.gov/careers/index.php.
Outstanding candidates may be considered for a Postdoctoral Fellowship. For more information about LANL's Postdoc Program, go to: https://www.lanl.gov/careers/career-options/postdoctoral-research/index.php
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
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.
Clearance: Q (Position will be cleared to this level). Selected applicants will be subject to a background investigation conducted by or on behalf of the Federal Government, and must meet eligibility requirements* for access to classified matter. This position requires a Q clearance. and obtaining such clearance requires US Citizenship except in extremely rare circumstances. Dependent upon the position, additional authorization to access classified information may be required, which may or may not be available to dual citizens. Receipt of a Q clearance and additional access authorization ultimately is a decision of the Federal Government and not of Triad.
New-Employment Drug Test: The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing. Although New Mexico and other states have legalized the use of marijuana, use and possession of marijuana remain illegal under federal law. A positive drug test for marijuana will result in termination of employment, even if the use was pre-offer.
Internal Applicants: Regular appointment employees who have served the required period of continuous service in their current position are eligible to apply for posted jobs throughout the Laboratory. If an employee has not served the required period of continuous service, they may only apply for Laboratory jobs with the documented approval of their Division Leader. Please refer to Policy Policy P701 for applicant eligibility requirements.
Incentive Compensation Program: For general program information refer to the Student Programs web page: https://www.lanl.gov/careers/career-options/student-internships/index.php
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 applyhelp@lanl.gov or call (505) 664-6947, opt. 3.
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