Lawrence Livermore National Laboratory



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Postdoctoral Research Staff Member-Energy Conversion and Storage Group

Location: Livermore, CA
Category: Post Docs
Job ID: 100961
Job Code: 
Date Posted: 

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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 within the Energy Conversion and Storage Group for a Postdoctoral Research Staff Member. The Energy Conversion and Storage Group seeks to gain fundamental and practical insight into combustion processes through numerical simulations and experiments and develop predictive combustion models and software that are fast enough to impact the engine design cycle.
 
Essential Duties
- Conduct research in and development of one or more of the following areas: fluid dynamics, heat transfer, reactive chemistry, numerical methods, and machine learning.
- Design, implement, and analyze techniques in one or more of the above areas.
- Document research, publish papers in peer-reviewed journals, and present results within the DOE community and at conferences.
- Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally.
- Reduce the time, resource cost, or increase accuracy of combustion simulations by designing efficient algorithms guided by applied mathematics and physics.
- Develop and apply numerical tools to simulate high efficiency clean combustion engine regimes and novel combustion systems by combining multidimensional fluid mechanics with chemical kinetics.
- Conduct detailed analysis of high efficiency clean combustion engine regimes and novel combustion processes.

Qualifications
- Recent PhD in Engineering, Applied Science, or related field.
- Demonstrated solid background and expertise in one or more of the following areas: fluid dynamics, heat transfer, reactive chemistry, numerical methods, and machine learning.
- Demonstrated ability to effectively perform independent research.
- Comprehensive knowledge in analysis, modeling, and simulation tools (e.g. CAD, CFD, FEA).
- Ability to thrive autonomously; be pro-active in solving inevitable road blocks, have a persistent attitude, and self-train when necessary.
- Demonstrated fundamental verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
- Experience working within a multidisciplinary engineering team.

Desired Qualifications
- Experience in writing technical reports, journal articles, and high-quality research proposals.
- Demonstrated project management skills.
- Experience with C++, Python, R, Linux, Unix, GPUs, and massively parallel algorithm development, experiment design and data acquisition.

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

Anticipated Clearance Level:  None.

 

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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