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.