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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 looking for individuals that demonstrate an understanding of working in partnership with team peers, who engage, advocate, and contribute to building an inclusive culture, and provide expertise to solve challenging problems.
We have an opening for a Postdoctoral Research Staff Member to conduct research and development in the direct ink write additive manufacturing of carbon fiber materials. You will be part of an interdisciplinary team of software engineers, materials scientists, structural engineers, and chemists 1) applying existing and new additive manufacturing techniques and simulation methodologies to enhance the fundamental understanding of process-structure-property-performance interactions and 2) accelerating the qualification of additively manufactured materials and components. This position is in the Computational Engineering Division (CED) within the Engineering Directorate.
In this role you will
- Develop software to generate CNC machining code (G-code toolpaths) from CAD models using advanced geometry algorithms.
- Generate software that is able to parse CNC machining code, simulate the dynamics of the printing process and visualize the simulation.
- Perform finite element simulations of the fabricated components to study their performance prior to printing.
- Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists and engineers within and external to the Laboratory.
- Collaborate with scientists, engineers, technicians, and machinists in a multidisciplinary team environment to accomplish research goals.
- Publish research results in peer reviewed scientific or technical journals and present results at external conferences seminars and/or technical meetings.
- Perform other duties as assigned.
- PhD in Computer Science, Mechanical Engineering, Materials Science, or related field.
- Experience in object-oriented programming for data analysis (e.g., Python, Matlab, C/C++).
- Experience in generating and parsing additive manufacturing toolpaths code (G-code).
- Experience with surface and volume mesh generation, e.g., STL files, CAD surface and volumetric data bases, computer graphics.
- Experience developing independent research projects, including publication of peer-reviewed literature.
- Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
- Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.
Qualifications We Desire
- Knowledge of or experience in CNC toolpath generation for additive manufacturing.
- Knowledge of or experience in advanced algorithms for geometry manipulation.
- Knowledge of or experience in level set methods.
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
Pre-Employment Drug Test
External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Pre-Placement Medical Exam
A job related pre-placement medical examination may be required.
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.
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