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Uncertainty Quantification and Machine Learning at Extreme Scale - Postdoctoral Researcher

Entry Level | Full-time
Postdoctoral/Fellowship | livermore, CA | 08/19/2021

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Company Description

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.

Job Description

We have an opening for a Postdoctoral Researcher to perform research in uncertainty quantification (UQ) and machine learning at extreme scale, with a focus on exploiting highly accurate hierarchies of coarse (upscaled) discretization schemes (typically developed based on algebraic multigrid) utilizing high-performance computing. You will support ongoing projects in adaptive schemes that can be effectively reused in Markov Chain Monte Carlo simulation of multi-physics phenomena with uncertain input data that are common in subsurface flow models, as well as in various (non-PDE) network applications (such as power grid).  This position is in the Center for Applied Scientific Computing (CASC) within the Computation Directorate.

 

In this role you will

  • Research and develop methods for UQ simulations exploiting both highly accurate coarse (upscaled) finite element schemes (under development at LLNL) and machine learning algorithms.
  • Develop prototype software utilizing high-performance computing to evaluate novel UQ.
  • Publish research results in external peer-reviewed scientific journals and participate in conferences and workshops.
  • Present formal and informal overview of research progress at group meetings.
  • Contribute to grant proposals and collaborate with others in a multidisciplinary team environment, including academic and industrial partners, to accomplish research goals.
  • Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory.
  • Perform other duties as assigned.

Qualifications

  • Ph.D. in Computer Science, Mathematics, or related field.
  • Experience in developing, implementing and applying uncertainty quantification, machine learning, Bayesian inference methods and algorithms.
  • Experience programming in Python, C/C++, or similar languages, and in a Unix/Linux environment.
  • Research experience in one or more of the following areas: numerical PDEs, finite elements and multilevel solvers, Markov chain Monte Carlo, as well as basic knowledge of probability and statistics with application to UQ.
  • Ability to conduct high quality research and to develop implementations of sophisticated algorithms to evaluate the results.
  • Demonstrated publication record in peer-reviewed journals and/or conferences.
  • Analytical and problem-solving skills necessary to craft creative solutions to independently solve complex problems.
  • Proficient verbal and written communication skills to effectively collaborate in a team environment, present and explain technical information to technical as well non-technical audience, document work and write research papers.

Qualifications We Desire

  • Experience in specialized (element or aggregation based) algebraic multigrid, Monte Carlo and MCMC simulations, and their implementation on neural networks.
  • Experience implementing algorithms for distributed computers or multi-core CPUs or GPUs.
  • Experience with MPI and/or OpenMP.

Additional Information

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

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.

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.

If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.

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