Livermore, CA, USA
Lawrence Livermore National Laboratory
Science and Technology on a Mission!
For more than 60 years, the Lawrence Livermore National Laboratory (LLNL) has applied science and technology to make the world a safer place.
We are looking for a postdoctoral researcher to perform research in the development and analysis of large-scale graph analytics, algorithms, high performance data-centric architectures, system software, and libraries. You will have research opportunities in the development of techniques to exploit advanced memory technologies, including persistent memory; advanced data structures and algorithms for leveraging new data-centric systems; system software and library development to optimize performance; and algorithms and applications tuned to exploit advanced memory and related CPU architectural enhancements. This position is in the Computation Directorate within the Center for Applied Scientific Computing Division (CASC).
Essential Duties
- Research, design, implement, and evaluate advanced graph analytics on data-centric architectures, parallel measurement and analysis techniques.
- Document research by publishing papers in peer-reviewed media and presenting papers within the DOE community and at conferences and contribute to group grant proposals, including proposal presentations and preparation of proposals.
- Interact with co-design teams to better understand application needs and to evaluate proposed components.
- Pursue independent but complementary research interests and interact with a broad spectrum of scientists internally and externally to define and carry out the research.
- Participate in the establishment of future research directions
- Interact with LLNL production staff to integrate with LLNL production systems.
- Perform other duties as assigned.
Qualifications
- Recent Ph.D. in computer science, applied mathematics or a related field.
- Expertise in parallel computing, architecture, system software, or related field.
- Experience with data-centric computing applications, data mining, graph algorithms, or machine learning.
- Demonstrated effective verbal and written communication skills necessary to interact with a multi-disciplinary research team, author technical and scientific reports and papers, and deliver scientific presentations.
- Demonstrated interpersonal communication skills necessary to work effectively in a team environment.
- Demonstrated experience developing independent research and experience in the identification of complex problems and solutions in a creative and timely manner.
Desired Qualifications
- Knowledge of C/C++ and familiarity with HPC systems.
- Ability to analyze capabilities and limitations of deterministic and random algorithms
- Knowledge of parallel programming models such as MPI, OpenMP, or GPUs.
Pre-Employment Drug Test: External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test.
Anticipated Clearance Level: None.
Note: Relisted position. Originally listed on 10/24/2016. Previous candidates need not reapply.
This is a one year Postdoctoral appointment with the possibility of extension to a maximum of three years. Eligible candidates are recent PhDs within five years of the month of the degree award at time of hire date.
About Us
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 $1.5 billion, employing approximately 6,000 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.