Sandia National Laboratories is the nation's premier science and engineering lab for national security and technology innovation with major facilities in Albuquerque, New Mexico and Livermore, California.  We are a world-class team of scientists, engineers, technologists, post docs, and visiting researchers all focused on cutting-edge technology, ranging from homeland defense, global security, biotechnology, and environmental preservation to energy and combustion research, computer security, and nuclear defense.  To learn more, please visit our website at www.sandia.gov.

Sandia National Laboratories is searching for a Postdoctoral Appointee in Optimization and UQ R&D for the Optimization and Uncertainty Quantification Department located in our Albuquerque, NM facility. This position is a temporary, full-time opportunity. Currently, this position does not require a DOE-granted security clearance.

Department Description
The mission of the Optimization and Uncertainty Quantification Department at Sandia National Laboratories is to provide leadership in the research, development, and application of scientific optimization and UQ algorithms and software. Staff members work in a collaborative, highly multidisciplinary, team-based environment. The department is well known for DAKOTA, a multilevel, parallel, object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analyses. Broad partnerships are maintained at many levels, and collaborations are actively carried out with other government institutions, universities and industry. The department frequently hosts students and faculty from world-class universities for extended visits through the Computer Science Research Institute (CSRI). 

[see:   http://www.cs.sandia.gov/CSRI/]

Job Summary
This position involves activities spanning fundamental algorithm research, object-oriented software development, and development and application of methodologies in the broad area of optimization and uncertainty quantification. The individual will work with a strong and growing multidisciplinary team to study the spectral and statistical properties of sample distributions, and algorithms for generating sample designs for optimization and uncertainty quantification. The position requires a highly motivated individual with a PhD in engineering, statistics or a related discipline, and strong academic and publication records. Also required is expertise in Bayesian statistics and Fourier analysis. Additionally, a versatile background in one or more of the following areas is desired: Mathematical modeling and optimization, sampling, meshing, signal and image processing, parameter estimation, wavelets, filter design, pattern recognition, and neural networks. The candidate should be proficient at programming and fluent in C++ and object oriented software design principles, and software prototyping and experimentation, for example in MATLAB. Research results are expected to be published in reports and leading technical journals, and presented at technical workshops and conferences. Results, when appropriate, are to be implemented into our optimization/UQ and mathematical software toolkits such as Trilinos and DAKOTA. These activities will be carried out in a collaborative, team-based environment. 

Required
·   Ph.D. in the field of Computational Science, such as mathematics, statistics, engineering or a related discipline, and have academic or work experience specializing in the targeted areas

·   Research experience, as evidenced by technical publications and presentations, in the design and analysis of algorithms in the areas of optimization and uncertainty quantification

·   Demonstrated expertise in Bayesian statistics and Fourier analysis

·   Advanced C++ programming skills, with relevant software development experience in a team-based environment

Desired
·   Proven ability to work in a collaborative, multi-disciplinary research and software development environment

·   Understanding of formal software quality engineering principles

·   Experience and/or training in one or more engineering application areas

·   Knowledge and/or experience in sampling, meshing, signal and image processing, wavelets, filter design, pattern recognition, and neural networks

·   Excellent written and oral communication skills

·   Knowledge of advanced HPC architectures and operating systems

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