Research Computational Scientist

Oak Ridge National Laboratory•Oak Ridge, TN

46d•Onsite

About The Position

The Multiphysics Modeling and Flows (MMF) Group in the Computational Sciences and Engineering Division is seeking a Research Computational Scientist with expertise in computational mesh generation to generate validated computational results that are used for large-scale, physics-based simulations for a variety of applications. MMF is a computational multiphysics modeling group at the forefront of deploying novel computational engineering techniques for problems that are critical to U.S. national and energy security. We utilize our expertise in numerical discretization techniques, high performance computing, mesh generation, and geometry representation for a wide variety of physics applications. Our intention is to integrate computational modeling with experimental methods and provide innovative solutions for technology growth. We aspire to help generate technical artifacts (patents) as well as scientific artifacts (journal articles). Almost all of our simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor-intensive process because it is shown to be a leading driver of computational accuracy. As a result, for certain use cases of interest to ORNL, we are in need of an applications engineer capable of generating meshes and accurate solutions to complex problems.

Requirements

An MS in mathematics, computer science, engineering or a related field or a PhD in mathematics, computer science, engineering with relevant experience.
Ability to obtain and maintain a clearance from the Department of Energy.
Must pass a pre-placement drug test and participate in an ongoing random drug testing program.

Nice To Haves

Experience with mesh generation/CFD applications for unstructured meshes and/or finite element methods
Experience with CFD discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments
Experience with parallel computing environments, HPC in a Linux environment
Experience with surrogate modeling
Experience with data analytics techniques
Familiarity with C++ and GPU programming
Familiarity with Python programming
Experience with modern software development practices to ensure code quality
Experience with computational geometry
Excellent written and oral communication skills
Ability to work with a measure of autonomy and a willingness to participate creatively in a collaborative team environment

Responsibilities

Working with subject matter experts to understand and refine new CFD application and/or CFD discretization techniques.
Leading the implementation of these techniques into large-scale, automatic computational work-flows with particular attention paid to computational accuracy.
Focusing initially on unstructured CFD discretization and conformal mesh generation techniques for applications requiring boundary-layer (i.e., large aspect ratio) meshing capabilities.
Exploring adaptive meshing for design/shape optimization as well as solution optimization.
Engaging with the broader community of industry, national labs, and academic partners to understand and advance the state-of-the-art in CFD applications and work-flows.