Uncertainty Quantification for Surrogate Models Postdoctoral Researcher

We have an immediate opening for a Postdoctoral Researcher to perform research and development as well as verification and validation of uncertainty quantification (UQ) methods for surrogate models.  Deep Gaussian processes as well as scalable Gaussian processes are of particular interest.  You will work independently as a technical expert and will interact with other researchers in statistics, UQ, applied mathematics, and machine learning/AI.  This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Principal Directorate.

In this role you will

• Conduct basic research in efficient Gaussian processes to understand conditions under which their resulting uncertainties agree with other UQ metrics for AI surrogate models.
• Collaborate with others in a multidisciplinary team environment to accomplish research goals including industrial and academic partners.
• Develop, implement, validate, and document specialized analysis software tools and models as required.
• Organize, analyze and publish research results in peer-reviewed scientific or technical journals and present results at external conferences seminars and/or technical meetings.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
• Perform other duties as assigned.

• Ph.D. in Statistics, Applied Mathematics, or a related field.
• Experience with deep Gaussian processes.
• Knowledge of ongoing work in scalable Gaussian processes.
• Experience with functional data.
• Knowledge of AI surrogates (e.g., neural networks) and associated UQ methods.
• Experience using programming skills in at least one prototyping language R/Matlab/Python.
• Knowledge of an ML library (TensorFlow, PyTorch, or JAX).
• Experience developing independent research projects as demonstrated through publication of peer-reviewed literature.
• Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
• Effective initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.

Desired Qualifications (optional)

• Familiarity with active learning/sequential design
• Experience with splines and associated UQ methods
• Experience with high-performance computing systems (i.e., parallel programming libraries such as MPI)
• Eligibility for a Department of Energy (DOE) Q-level clearance

#LI-Hybrid

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

• Included in 2025 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
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None required.  However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.  This process includes completing an online background investigation form and receiving approval of the background check.  (This process does not apply to foreign nationals.) 

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External applicant(s) selected for this position must 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.

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