Uncertainty Quantification for Surrogate Models Postdoctoral Researcher

Job description

Company Description Join us and make YOUR mark on the World! Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. Job Description 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. Qualifications 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 Pay Range $138,480 Annually This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. Additional Information #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 2026 Best Places to Work by Glassdoor! Flexible Benefits Package 401(k) Relocation Assistance Education Reimbursement Program Flexible schedules (*depending on project needs) Our values - visit https://www.llnl.gov/inclusion/our-values Security Clearance 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. 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