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Machine-Learning Driven Prediction of Next Generation Additive Manufacturing Inks - Postdoctoral Researcher
Postdoctoral/Fellowship | livermore, CA | 03/03/2021
Job Code: PDS.1 Post-Dr Research Staff 1
Organization: Physical and Life Sciences
Position Type: Post Doctoral
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 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 in the area of machine learning driven atomistic simulations in support of additive manufacturing (AM) of organics, including energetic materials. The primary research thrust of this project involves the predictive design of new AM inks with tailored mechanical (and chemical) properties produced under dynamic compression. The technical thrust of this position will entail the design of a concurrent, multi-scale modeling approach that allows for simulation from the quantum-scale to molecular dynamics and further coarse-grained models, coupled by material specific features learned during the course of a simulation. This position is in the Materials Dynamics and Kinetics research group of the Materials Science Division.
In this role you will
- Perform quantum simulations of condensed organic matter under dynamic compression.
- Conduct research in molecular dynamics force field development, including machine learning approaches.
- Use unsupervised learning approaches to characterize the physical-chemical phase space of organic mixtures..
- Contribute to the conception, design, and execution of research to related to the study of materials under extreme thermodynamic conditions.
- Document research; publish papers in peer-reviewed journals, and present results within the DOE community and at national and international conferences.
- Pursue independent but complementary research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
- Collaborate with scientists in a multidisciplinary team environment to accomplish research goals.
- Perform other duties as assigned.
- PhD in Physics, Chemistry, Chemical Engineering, Materials Science, or related field.
- Experience in first-principles simulation techniques of condensed phases.
- Experience in programming in C/C++, Fortran, or an equivalent high-level language. Knowledge of a scripting language is a plus.
- Experience with machine learning approach as applied to materials science problems, including representation learning approaches for feature extraction.
- Ability to develop independent research projects as evidenced through publication of peer-reviewed literature.
- Proficient written and verbal communication skills with demonstrated capabilities to author technical and scientific reports and publications and deliver scientific presentations.
- Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.
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. Most positions will require a DOE L or Q clearance (please reference Security Clearance requirement). If you are selected, 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. If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted. 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.
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