Associate Computer Science Jobs in New Mexico (NOW HIRING)

Job Summary:
Los Alamos National Laboratory is a multidisciplinary research institution engaged in strategic science on behalf of national security. They are seeking a postdoctoral research associate to work at the intersection of machine learning and Earth system science, developing 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.
• Exposure to multiple application domains, including ocean, sea ice, coastal hazards, terrestrial hydrology, permafrost, ice-sheet impacts, atmospheric extremes, and human-system risk.
• Opportunities for cross-disciplinary collaboration, scientific workshop organization, and conference participation.
Qualifications:
Required:
• 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.
• 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.
Preferred:
• 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.
Company:
Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is Founded in 1943, the company is headquartered in Los Alamos, USA, with a team of 10001+ employees. The company is currently Late Stage.