Earth System Modeling Scientist
Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in science and engineering on behalf of national security. The Computational Physics and Methods group is seeking qualified applicants in the areas of artificial intelligence/machine learning for Earth-system predictability and modeling. Candidates with experience in modeling and analyzing climate processes using innovative artificial intelligence/machine learning techniques are especially encouraged to apply.
The successful candidate will contribute to development and application of the Energy Exascale Earth System Model (E3SM), the DOE’s new Earth system model, to address science questions relevant to DOE’s missions. LANL is focused on the role of ocean and ice systems in high-latitude and global climate, marine biogeochemistry, land-river-ocean interactions and the impacts of sea-level rise. You will be expected to perform outstanding research in at least one of these areas, including using E3SM for fully coupled, global, variable-resolution simulations of the Earth system.
Working as an integral member of one or more multidisciplinary teams that study Earth’s high-latitude climate and coastal systems, you will:
- Advance understanding of predictability of the high-latitude and/or coastal Earth systems by developing and applying innovative approaches to improve the initialization of climate models
- Analyze large data sets, including large ensembles of climate models and build predictive models based on observations and model simulations
- Present progress to the team, project management, sponsors and the scientific community through peer-reviewed publications and conference presentations
- Ph.D. in climate science, applied mathematics, computer science, physics or related field
- Experience with artificial intelligence and machine learning approaches
- Demonstrated knowledge of the dynamics of the climate system or its components
- Demonstrated excellence in scientific research as evidenced by a strong record of peer-reviewed publications
- Ability to work effectively in interdisciplinary teams
- Excellent oral and written communications skills
- Demonstrated initiative for continuous learning and development in new areas of research
- Experience in coastal, ocean or Earth system modeling or model analysis
- Experience in artificial intelligence/machine learning that could be applicable to coastal or estuarine modeling (as evidenced by scientific code releases)
- Experience mentoring students, postdocs or junior staff, as well as developing and writing successful research grant proposals
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Los Alamos National Laboratory is more than a place to work. It is a catalyst for discovery, innovation and achievement. It’s one of the reasons we attract world-class talent who contribute greatly to our outstanding culture. Professional development, work/life balance and a diverse and inclusive team foster lasting career satisfaction. Our onsite cafeterias and medical, fitness and breastfeeding facilities, education assistance and generous compensation and benefits reflect our commitment to providing our people with all they need for personal and professional growth.
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Los Alamos National Laboratory is an equal opportunity employer and supports a diverse and inclusive workforce. All employment practices are based on qualification and merit, without regards to race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation or preference, 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 federal 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 such an accommodation, please send an email to email@example.com or call 1-505-665-4444 option 1.