Lawrence Livermore National Laboratory



Postdoctoral Research Staff Member

Location:  Livermore, CA
Category:  Post Docs
Organization:  Computation
Posting Requirement:  External Posting
Job ID: 103557
Job Code: Post-Dr Research Staff 1 (PDS.1)
Date Posted: March 27 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 are looking for a Postdoctoral Researcher to perform research in the area of data analysis and machine learning to develop new techniques to understand uncertainties in complex workflows and how to match and calibrate large-scale parallel simulations to diverse experimental data and multi-fidelity simulations. You will support ongoing efforts concerned with history matching of simulations and related topics such as metric learning, high dimensional analysis, workflows and adaptive sampling.  This position is in the Computation Directorate within the Center for Applied Scientific Computing (CASC) Division.

Essential Duties
- Conduct independent research and development in one or more areas of computer science of interest to CASC (machine learning, uncertainty quantification, topological analysis, adaptive sampling, information visualization).
- Develop software to evaluate novel computational techniques.
- Participate in the establishment of future research directions and contribute to group grant proposals, including proposal presentations and preparation of proposals that will provide future research opportunities in the field.
- Document research by publishing papers in peer-reviewed media and presenting papers within the DOE community and at conferences.
- Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to define and carry out the research.
- Perform other duties as assigned.

Qualifications
- PhD in Computer Science or a related field.
- Experience in modern machine learning environments (TensorFlow, Kieras, etc.).
- Experience in machine learning, uncertainty quantification, topological analysis, statistics, adaptive sampling information visualization, or a related field.
- Experience in application development.
- Experience in C, C++, and python
- Experience with UNIX tools, version control (subversion, git).
- Demonstrated ability to conduct high quality research and to develop implementations to evaluate the results.
- Effective verbal and written communication skills necessary to interact with a multi-disciplinary research team, author technical and scientific reports and papers, and deliver scientific presentations.

Desired Qualifications
- Experience with graphical user interfaces.
- Experience working with and running Fortran programs.
- Experience in application areas or numerical algorithms.

Pre-Employment Drug Test:  External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test.
 

Security Clearance:  None required.

 

Note:   This is a one 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 $1.5 billion, employing approximately 6,000 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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