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Predictions of Precipitation and Extremes - Postdoctoral Research Staff Member
Postdoctoral/Fellowship | livermore, CA | 10/18/2021
Job Code: PDS.1 Post-Dr Research Staff 1
Organization: Physical and Life Sciences
Position Type: Post Doctoral
Security Clearance: None (however, assignments longer than 179 days require a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable
Join us and make YOUR mark on the World!
Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.
We are looking for individuals that demonstrate an understanding of working in partnership with team peers, who engage, advocate, and contribute to building an inclusive culture, and provide expertise to solve challenging problems.
We have an opening for a Predictions of Precipitation and Extremes Postdoctoral Researcher to conduct research on improving the predictions of precipitation and extremes over the US in the Energy Exascale Earth System Model (E3SM). You will work with a team of scientists to conduct model diagnosis with application of machine learning/artificial intelligence (ML/AI) techniques to better understand underlying problems in E3SM model physics critical to the prediction of precipitation and its extremes. This position is in the Atmospheric, Earth & Energy Division of the Physical Life Sciences Directorate. This is a two-year postdoctoral appointment with the possibility of extension to a maximum of three years.
In this role you will
- Conduct original research on model diagnosis with application of ML/AI techniques to better understand underlying problems in E3SM model physics critical to the prediction of precipitation and its extremes.
- Conduct hypothesis-testing experiments with E3SM on high-performance computing environment.
- Document research by publishing papers in peer-reviewed journals and present technical results at scientific conferences.
- Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
- Perform other duties as assigned.
- Ph.D. in atmospheric science, physics, applied math, engineering, or a closely related discipline.
- Experience in one or more of the following areas: precipitation processes, extremes, atmospheric convection, global climate modeling, and application of ML/AI techniques to climate sciences.
- Proficient in at least one scientific programming language for modeling and climate data analysis (e.g., Python, Fortran, C++).
- Experience with large model and observational datasets, modern programming environments, and visualization techniques.
- Proficient verbal and written communication skills, as evidenced by published results and presentations.
- Interpersonal skills necessary to interact with a diverse set of scientists, engineers, and other technical and administrative staff.
Qualifications we desire
- Experience with analysis of precipitation and its extremes with global climate model simulations and observations.
- Knowledge of parameterizations of precipitation and convection in climate models.
Why Lawrence Livermore National Laboratory?
- Included in 2021 Best Places to Work by Glassdoor!
- Work for a premier innovative national Laboratory
- Comprehensive Benefits Package
- Flexible schedules (*depending on project needs)
- Collaborative, creative, inclusive, and fun team environment
Learn more about our company, selection process, position types and security clearances by visiting our Career site.
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Equal Employment Opportunity
LLNL is an affirmative action and equal opportunity employer that values and hires a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
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