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The Applied Statistics Group is seeking motivated and talented statisticians to assist in conducting basic and applied research in uncertainty quantification, statistical modeling, large scale parameter estimation, and advanced data analysis. At Lawrence Livermore National Laboratory (LLNL), we are developing game changing technologies enabled by our world class supercomputing facilities to represent and analyze the largest datasets in support of our national security and national science applications. These positions are in the Computational Engineering Division (CED), within the Engineering Directorate.
These positions will be filled at either the SES.1 or SES.2 level depending on your qualifications. Additional job responsibilities (outlined below) will be assigned if you are selected at the higher level.
- Conduct data processing activities, including but not limited to understanding the data through the use of visualization and statistical analysis, data curation, and both fitting and evaluating state-of-the-art statistical models.
- Conduct surveys of state-of-the-art statistical modeling, uncertainty classification, Bayesian inference, design of experiments, statistical machine learning models, and algorithms relevant to the problem being addressed.
- Contribute to research efforts in statistics, uncertainty quantification, machine learning, and data analytics that enable development of new state-of-the-art solutions for Laboratory problem domains.
- Conduct implementation, training and validation of proposed new state-of-the-art models and algorithms for Laboratory problem domains.
- Contribute to the integration of algorithms within larger programmatic systems that require these capabilities.
- Participate in interactions with inter-organizational contacts and/or external customers.
- Assist in representing the organization by providing input on technical issues for specific projects including preparing and presenting technical reports.
- Perform other duties as assigned.
In Addition at the SES.2 Level
- Research, develop, and apply solutions to moderately complex statistical problems of programmatic interest.
- Publish papers in peer reviewed journals and present results at scientific meetings and conferences.
- Contribute to proposals.
- Bachelor’s degree in Statistics, Applied Mathematics, Computer Science, Computational Engineering, Electrical Engineering, or the equivalent combination of education and related experience.
- Fundamental knowledge of and/or experience developing and applying algorithms in one or more of the following research areas: Bayesian inference, uncertainty quantification, unsupervised feature learning, active learning, reinforcement learning, ensemble methods, scalable online estimation, and probabilistic graphical models.
- Experience in the broad application of one or more higher level programming languages such as Python, Java/Scala, Matlab, R or C/C++.
- Experience with one or more machine learning libraries such as scikit-learn, MLlib,TensorFlow, PyTorch, Keras, Caffe or Theano.
- Experience working independently under general direction within the scope of an assignment and use sound judgment in determining methods, techniques, and evaluation criteria.
- Sufficient verbal and written communication skills necessary to effectively collaborate in a team environment and present technical ideas/results.
In Addition at the SES.2 Level
- Comprehensive knowledge and experience with statistical modeling, density estimation, inference, and uncertainty quantification.
- Experience developing well-written, documented, and version-controlled code.
- Proficient verbal and written communication skills to collaborate in a multi-disciplinary team environment, publish and present technical ideas and inform management.
- Master’s degree in Statistics, Applied Mathematics, Computer Science, Computational Engineering, Electrical Engineering, or the equivalent combination of education and related experience.
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 a Department of Energy (DOE) Q-level clearance.
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. Q-level 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.
Note: This listing has multiple openings; these are Career Indefinite positions. Lab employees and external candidates may be considered for these positions.
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 $2.1 billion, employing approximately 6,800 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.