Postdoctoral Appointee - Machine Learning for Power Systems - Military Veterans
Lemont, IL, United States
The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis is looking for a dedicated Postdoctoral Researcher. This role is ideal for someone passionate about solving challenges on the future power grid advancing the integration of distributed energy resources (DER) and renewable energy. The selected candidate will be involved in modeling, analysis and control of electric power distribution and transmission system, applying state of the art machine learning (ML) and deep learning algorithms to develop cybersecurity, optimization, and control solutions for grid applications.
Collaborate with multidisciplinary teams to develop innovative solutions and technologies for complex challenges in energy systems and power grid domains.
Present research findings through seminars, journal articles, technical reports, and at conferences and workshops.
Contribute to open-source software development initiatives for Department of Energy projects.
Position Requirements
Ph.D. in Electrical Engineering, Computer Science, Operations Research, or a related field.
Demonstrated expertise in control and optimization techniques, cybersecurity measures for power systems, machine learning applications in energy settings, and a comprehensive understanding of power distribution systems.
Working knowledge of electric power distribution systems, DER operations, and grid modeling and simulation.
Proficiency in Python and OpenDSS.
Familiarity in scripting using at least one ML framework (Keras, TensorFlow, PyTorch, or scikit-learn) and data engineering package (e.g. pandas).
Solid foundation in mathematics/statistics with experience in cyber-physical systems modeling and analysis.
Ability to work both independently and collaboratively in a team environment.
Demonstrated experience in interdisciplinary research.
Proven problem-solving and analytical skills.
Strong communication skills, both oral and written.
Record of publications in high-impact journals and experience in proposal development.
A successful candidate must have the ability to model Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Candidates will be required to work in at least 3 of the following areas:
Develop optimization and control tools for power distribution systems and test them on real-time hardware in the loop grid simulators.
Work with transmission and distribution grid simulation software such as OpenDSS, MATPOWER, PSS/E etc to run simulations and collect simulation data.
Develop data pipelines that can collect historical and simulation data for use with ML model training.
Develop and implement machine learning algorithms for applications such as real-time data fusion, anomaly detection, and control in microgrids.
Perform exploratory data analysis and generate analytics from power grid measurements.
Design and implement game theory and reinforcement learning-based solutions for power grid control problems or cybersecurity applications.
Preferred Qualifications:
Familiarity with power system analysis software such as MATPOWER, PSS/E, OpenDSS or similar.
Experience in developing ML solutions using reinforcement learning and/or advanced deep learning architectures.
Familiarity with federated learning, and game theoretic modeling.
Experience or familiarity with hardware-in-the-loop is helpful but not required.
Application Procedure:
Interested candidates should submit:
A detailed CV
A cover letter describing your research experience and interests.
GitHub profile (if available)
Job Family
Postdoctoral Family
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.
All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.
#J-18808-Ljbffr