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Computational Optimization Engineer
Engineering | livermore, CA | 06/11/2021
Job Code: SES.2 Science & Engineering MTS 2 / SES.3 Science & Engineering MTS 3
Position Type: Flexible Term
Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable
Join us and make YOUR mark on the World!
Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.
We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is essential to innovation and creativity for continued success of the Laboratory’s mission.
We have multiple openings for Computational Engineers to conduct basic and applied research in the optimization, simulation, and design of physical and engineered systems. You will work collaboratively and independently to extend and develop physics-based design optimization tools. The team is working to both extend the state-of-the-art and support Laboratory missions. These positions are in the Computational Engineering Division (CED) of the Engineering Directorate.
These positions will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.
In this role you will
- Develop and implement HPC algorithms and simulations for the optimal design of engineering systems governed by nonlinear, transient, multiscale, and multiple physical phenomena that include uncertainties in geometry and material properties.
- Contribute to solving challenging design optimization problems.
- Develop forward model, continuum simulations to solve the governing partial differential equations of physical systems necessary for advanced design.
- Document research through presentations and peer-reviewed journal articles and contribute to identifying future research directions and proposals that will provide opportunities in the field.
- Perform other duties as assigned.
Additional job responsibilities, at the SES.3 level
- Guide the establishment of future research directions and influence proposals that will provide future research opportunities in the field.
- Identify and solve challenging design optimization problems highlighting capabilities and establishing credibility with mission customers.
- Ph.D. in Engineering, Mathematics, Computational Science, or a related field, or the equivalent combination of education and related experience.
- Knowledge of shape/topology optimization techniques and algorithms.
- Experience developing simulation tools using continuum approaches (e.g., finite elements) for physical systems.
- Experience programming skill in C/C++/FORTRAN and scripting languages including Python/Matlab.
- Comprehensive experience with explicit design sensitivity analysis via direct (forward) and adjoint (backward) methods.
- Knowledge of massively-parallel computing and one or more associated parallel programming interfaces, such as MPI, OpenMP, or CUDA.
- Proficient verbal and written communication skills needed to effectively collaborate in a team environment, present and explain technical information, document work, prepare and present successful proposals and high-quality research papers.
Additional qualifications at the SES.3 level
- Advanced knowledge of shape/topology optimization techniques and algorithms.
- Significant experience developing simulations tools using continuum approaches (e.g., finite elements) for physical systems and advanced programming skill in C/C++/FORTRAN and scripting languages including Python/Matlab.
- Significant experience with explicit design sensitivity analysis via direct (forward) and adjoint (backward) methods and experience with massively-parallel computing and one or more associated parallel programming interfaces, such as MPI, OpenMP, or CUDA.
- Advanced verbal and written communication skills needed to effectively collaborate in a team environment, present and explain technical information, document work, prepare and present successful proposals and high-quality research papers.
Qualifications We Desire
- Exposure to machine learning and other data science techniques.
Why Lawrence Livermore National Laboratory?
- Included in 2021 Best Places to Work by Glassdoor!
- Work for a premier innovative national Laboratory
- Comprehensive Benefits Package
- Flexible schedules (*depending on project needs)
- Collaborative, creative, inclusive, and fun team environment
Learn more about our company, selection process, position types and security clearances by visiting our Career site.
LLNL is a Department of Energy (DOE) and National Nuclear Security Administration (NNSA) Laboratory. Some positions will require a DOE L or Q clearance (please reference Security Clearance requirement above). If you are selected and a clearance is required, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing. An L or Q clearance requires U.S. citizenship. For additional information please see DOE Order 472.2.
Equal Employment Opportunity
LLNL is an affirmative action and equal opportunity employer that values and hires a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form.
California Privacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.