Uncertainty Quantification and Machine Learning - Postdoctoral Researcher

Lawrence Livermore National Laboratory
Livermore, CA 94550

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Come join Lawrence Livermore National Laboratory (LLNL) where we apply science and technology to make the world a safer place; now one of 2019 Best Places to Work by Glassdoor!

We have an opening for a Postdoctoral Researcher to perform research in the areas of uncertainty quantification and machine learning in support of a new Strategic Initiative focused on predicting materials degradation kinetics. You will work collaboratively with LLNL researchers to develop uncertainty quantification and machine learning algorithms and a framework that will establish the simulation-based and data-driven understanding in the areas of multiscale modeling of oxidation, hydriding, and electrochemical corrosion in metals. This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Directorate.

Essential Duties
- Research new algorithms and explore existing techniques in uncertainty quantification, machine learning, data assimilation, and stochastic model reduction.
- Interact with collaborators both internal and external to the Laboratory on understanding and predicting initiation of hydriding, oxidation, and electrochemical corrosion.
- Contribute to the conception, design, and execution of research to related to the study of materials under extreme thermodynamic conditions.
- Apply state-of-the-art computational techniques on high performance computing platforms.
- Collaborate with computational and experimental scientists in a multidisciplinary team environment to accomplish research goals.
- Participate in the establishment of future research directions and contribute to group grant proposals, including preparation and presentation of proposals.
- Document complex research and development progress via technical reports, journal publications, and conference presentations and collaborate with a broad spectrum of scientists internally and externally to accomplish research goals.
- Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to define and carry out the research.

Qualifications
- Ph.D. in Applied Mathematics, Computational Mathematics, Computer Science or a related field.
- Experience in developing, implementing and applying uncertainty quantification, machine learning, Bayesian inference methods and algorithms.
- Ability to obtain domain knowledge in fields of application and to communicate effectively with subject matter experts.
- Ability to conduct high-quality independent research and to develop implementations to evaluate the results.
- Experience in programming in Python, C/C++, or similar languages, and in a Unix/Linux environment.
- Proficient verbal and written communication skills necessary to interact in a clear and concise manner, author technical and scientific reports and papers, and deliver scientific presentations.
- Ability to take the initiative and have interpersonal communication skills necessary to work effectively in a dynamic team environment.

Desired Qualifications
- Experience with multi-scale modeling and simulation of oxidation, hydriding, and electrochemical corrosion in metals.
- Experience in modeling of phase transformations involving microelasticity or plasticity effects.
- Experience in GPU programming, parallel programming, preferably in widely used parallel programing models, such as OpenMP, CUDA and/or MPI.

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 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 applyingcutting-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 $2.1 billion, employing approximately 6,800 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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Uncertainty Quantification and Machine Learning - Postdoctoral Researcher

Lawrence Livermore National Laboratory
Livermore, CA 94550

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