Lawrence Livermore National Laboratory

Postdoctoral Research Staff Member - Deep Learning and Climate Predictions

Location:  Livermore, CA
Category:  Post Docs
Organization:  Physical and Life Sciences
Posting Requirement:  External Posting
Job ID: 104332
Job Code: Post-Dr Research Staff 1 (PDS.1)
Date Posted: October 17 2018

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Science and Technology on a Mission!

For more than 60 years, the Lawrence Livermore National Laboratory (LLNL) has applied science and technology to make the world a safer place.

We have an opening for a Postdoctoral Research Staff Member who is excited to work at the interface of climate science, data science and machine learning. You will work with a team of experts in climate science, machine learning, data science, and computational science to develop and test new approaches to challenging problems in inter-seasonal climate forecasting; your contributions have the potential to make enormous societal impacts by predicting the availability of natural resources affected by changes in the climate system. This position is in the Atmospheric, Earth & Energy Division.

Essential Duties
- Conduct research and development to quantify predictors that influence climate resources in the Western U.S.
- Train, test, and validate deep learning models for inter-seasonal climate forecasts.
- Gather and process large climate data sets for training and testing of deep neural networks.
- Publish research results in internal reports, peer-reviewed scientific or technical journals, and present results at external conferences, seminars and/or technical meetings.
- Work independently and interact with a broad spectrum of scientists internal and external to the Laboratory.
- Perform other duties as assigned.

- Recent PhD in climate science, atmospheric science, environmental engineering, or closely related field.
- Experience in handling and analyzing large datasets.
- Experience in machine learning, statistical techniques, or time-series analysis.
- Proficient in a computer language used in data science (e.g., Python, R, or MATLAB).
- Ability to conduct high quality, independent research.
- Proficient verbal and written communication skills as evidenced by published results and presentations.
- Experience collaborating effectively with a team of scientists with diverse backgrounds.
- Ability to travel as required to coordinate research with internal and external collaborators and sponsors.

Desired Qualifications
- Experience in weather forecasts, seasonal climate forecasts, or decadal predictions.
- Experience in high performance computing environments.
- Experience in deep learning architectures.

Pre-Employment Drug Test:  External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test.  This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Security Clearance:  None required.


Note:   This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years.  Eligible candidates are recent PhDs within five years of the month of the degree award at time of hire date.

About Us

Lawrence Livermore National Laboratory (LLNL), located in the San Francisco Bay Area (East Bay), is a premier applied science laboratory that is part of the National Nuclear Security Administration (NNSA) within the Department of Energy (DOE).  LLNL's mission is strengthening national security by developing and applying cutting-edge science, technology, and engineering that respond with vision, quality, integrity, and technical excellence to scientific issues of national importance.  The Laboratory has a current annual budget of about $1.8 billion, employing approximately 6,500 employees.


LLNL is an affirmative action/ equal opportunity employer. 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, protected veteran status, age, citizenship, or any other characteristic protected by law.

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