Decentralized Optimization and Stochastic Programming for Power Systems Operations - Postdoctoral Researcher

Lawrence Livermore National Laboratory
Livermore, CA 94550

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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.

Essential Duties
- 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.

Desired Qualifications
- 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.

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:  None required.

However, if 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.)

For additional information, please see DOE Order 472.2


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.

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 $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.


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Decentralized Optimization and Stochastic Programming for Power Systems Operations - Postdoctoral Researcher

Lawrence Livermore National Laboratory
Livermore, CA 94550

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