Post-Doctoral Research Associate in uncertainty quantification
for Unsteady Turbulent flows
Sandia National Laboratories has an opening for a post-doctoral
research associate in its Engineering Sciences Center. This
position is a two-year appointment, with a possibility for future
extension, in the area of compressible fluid mechanics and
machine learning, and can begin immediately.
Sandia’s Engineering Sciences Center offers challenging and
important work relating to national security in R&D and
technology applications. Our primary mission supports U.S.
Department of Energy Defense Programs, and aerosciences
projects funded through the U.S. Department of Defense, DARPA, NASA, and industry. Projects span the Mach
number range from subsonic through hypersonic and involve systems ranging from aircraft-released ordinance to
reentry systems and rocket systems. We work synergistically with other organizations at Sandia in the areas of
flight testing, code development, and validation/uncertainty methods development.
A research opportunity exists in the area of uncertainty quantification (UQ) for turbulent flow simulations. One
research goal will be to utilize Machine Learning techniques and Direct Numerical Simulations to identify sources
of errors in wall-pressure fluctuation predictions arising from near-wall modeling and to quantify epistemic
uncertainties in such simulations. The post-doctoral associate will also collaborate on a project applying Machine
Learning to RANS turbulence model improvement with the goal of enabling adaptive model corrections.
The ideal candidate will have a strong background in Statistics, High Performance Computing/Distributed
Systems (MPI, CUDA, Hadoop, Spark) and Programming for Scientific Computing (C/C++/FORTRAN/Python).
Proficiency with techniques for assimilating and manipulating large datasets is required. Desired qualifications
include familiarity with any or all of the following disciplines: Fluid Mechanics and Turbulence Modeling,
Numerical Methods, Machine Learning theory and application, Neural networks, UQ/Bayesian methods.
Applicants must have a Ph.D. degree in aerospace, mechanical, computer science, applied mathematics or a
related engineering discipline, including an exemplary academic record. The successful candidate must have
excellent technical and time management skills, work effectively on multidisciplinary teams, and have
demonstrated oral and written communication skills. The position involves close collaboration with researchers at
Sandia’s Albuquerque and California sites and will involve regular travel between them.
The ability to obtain a Department of Energy security clearance is necessary for this position, which requires
United States Citizenship.
Please send an electronic resume, GPA for all degrees, publications list, and references to:
Jeffrey Payne
Sandia National Laboratories
P.O. Box 5800, Mailstop 0825
Albuquerque, NM 87185
(505) 844-844-4524
jlpayne@sandia.gov
Exceptional Service in the National Interest