Join us and make YOUR mark on the World!
Come join Lawrence Livermore National Laboratory (LLNL) where we apply science and technology to make the world a safer place; now one of 2020 Best Places to Work by Glassdoor!
We have multiple openings for Postdoctoral Researchers. You will engage in cutting edge research, design, and deployment of statistical methods to solve important decision and detection problems stemming from the Laboratory's core mission spaces. We invite you to join us if you have expertise in one of the following desired areas: Bayesian modeling, uncertainty quantification, analysis and design of computer experiments, statistical learning, statistical methods for “Big Data” or general statistical consulting.
If you are interested in career development, this is the perfect opportunity for you. Time is also available for completing journal articles for submission, learning new methods or programming languages, and exploring the variety of research activities across the Lab.
These positions are in the Computational Engineering Division (CED), within the Engineering Directorate.
- Conduct research in and development of one or more of the following areas: information retrieval and representation, cyber security, image and video analysis, design of computer experiments, climate modeling, energy analysis, computational biology, lasers, and optics.
- Design, implement, and analyze techniques in one or more of the above areas.
- Contribute to and actively participate with project scientists and engineers in the scope, planning, and formulating modeling/simulation efforts for physical, engineering, and computational systems in the areas of cyber security, biological and environmental threat detection, uncertainty quantification, and others.
- Develop, implement, validate, and document specialized analysis software tools and models as required.
- Collaborate with others in a multidisciplinary team environment to accomplish research goals including industrial and academic partners.
- Organize, analyze and publish research results in peer-reviewed scientific or technical journals and present results at external conferences seminars and/or technical meetings.
- Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
- Perform other duties as assigned.
- PhD in Statistics or related field.
- Experience using programming skills in at least one prototyping language R/Matlab/Python, as well as one of C/C++/Java to enable high performance statistical computation.
- Experience developing and applying advanced statistical/machine learning models and algorithms for one or more of the following settings: classification, clustering, anomaly detection, density estimation, pattern recognition, knowledge discovery.
- Experience as an innovative experimentalist with a broad range of experience in experimental design, techniques, and execution.
- Experience developing independent research projects as demonstrated through publication of peer-reviewed literature.
- Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
- Effective initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.
- Ability and desire to obtain substantial domain knowledge in fields of application and ability to communicate effectively with subject matter experts.
- Familiarity with algebraic statistics and statistical models for combinatorial/algebraic structures.
- Experience with human language technology, data mining, and self-supervised learning.
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.
Security Clearance: This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.
If you are selected and a security 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. L and Q-level clearances require 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.
If no security clearance is required, but your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check. (This process does not apply to foreign nationals.)
Note: This is a one year Postdoctoral appointment with the possibility of extension to a maximum of three years. Eligible candidates are those who have been awarded a PhD at time of hire date.
Lawrence Livermore National Laboratory (LLNL), located in the San Francisco Bay Area (East Bay), is a premier applied science laboratory that is part of the National Nuclear Security Administration (NNSA) within the Department of Energy (DOE). LLNL's mission is strengthening national security by developing and applying cutting-edge science, technology, and engineering that respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. The Laboratory has a current annual budget of about $2.3 billion, employing approximately 6,900 employees.
LLNL is an affirmative action/ equal opportunity employer. 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, protected veteran status, age, citizenship, or any other characteristic protected by law.