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Decentralized Optimization and Stochastic Programming for Power Systems Operations - Postdoctoral Researcher
Information Technology/Computing | livermore, CA | 01/25/2021
Job Code: PDS.1 Post-Dr Research Staff 1
Position Type: Post Doctoral
Security Clearance: None (however, assignments longer than 179 days require 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 Researcher to perform research in the area of decentralized optimization and related stochastic programming approaches for solving power systems operations problems. You will be part of an multi-institution interdisciplinary team that develops and implements novel numerical algorithms for core optimization problems found in power system operations, specifically leveraging algorithms such as Alternating Direction Method of Multipliers (ADMM) and related decomposition strategies for solving stochastic programming problems. These new algorithms will be used to develop novel operations strategies that provide improved execution resilience and yield advantages in terms of privacy and information sharing among market participants. This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Directorate.
In this role you will
- Research new decentralized optimization strategies for stochastic programming, leveraging MPI and large-scale compute clusters.
- Develop implementations of advanced decentralized and stochastic programming solvers in Python.
- Analyze and mitigate performance bottlenecks in developed software, considering scalability to tens of thousands of processors.
- Design and test domain-specific applications of advanced decentralized optimization strategies on key power systems operations problems.
- Participate in the establishment of future group research directions and contribute to group grant proposal efforts (including proposal preparation and presentation).
- Document key research findings via technical reports, journal publications, and conference presentations; coordinate with internal and external scientists to maximize impact and relevance of R&D.
- Pursue independent (but complementary) research interests and interact with a broad spectrum of internal and external scientists to define and execute research.
- Perform other duties as assigned.
- Ph.D. in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or a closely related field.
- Experience in numerical methods (including decomposition strategies) for stochastic programming and decentralized optimization, in particular those involving discrete decision variables.
- Experience in developing probabilistic characterizations of key inputs to stochastic programs, and interpretation and validation of stochastic programming solutions.
- Experience developing software in a high-level language such as Python, Julia, and C++ (Python preferred).
- Working knowledge of at least one algebraic modeling language (e.g., Pyomo, JuMP, AMPL, and GAMS) and of at least one widely used mathematical optimization solver (e.g., Gurobi, CPLEX, and Express).
- Ability to conduct high-quality independent research, develop software implementations of novel decentralized and stochastic decomposition solvers, and evaluate the effectiveness of developed solvers.
- Proficient verbal and written communication skills necessary to interact in a clear and concise manner, co-author technical and scientific reports and papers, and deliver scientific presentations.
- Initiative and interpersonal communication skills necessary to work effectively in a dynamic (multi-institutional) team environment.
Qualifications We Desire
- Experience with key power grid operations problems, including unit commitment, economic dispatch, and optimal (DC and AC) power flow.
- Experience with non-linear optimization solvers such as Ipopt.
Why Lawrence Livermore National Laboratory?
- Included in 2020 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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