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Post-doc in Cyber-Physical Anomaly Detection in Electric Power Systems

US Naval Academy

Annapolis, Maryland 21402
Job Type:
Job Status:
Full Time, Part Time
  • Control Systems
  • Energy
  • Power Engineering
  • Research
US Naval Academy
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Job Details

The Resilient Cyber-Physical Systems group at the United States Naval Academy is a joint effort between the departments of Electrical and Computer Engineering and Weapons and Systems Engineering.  The group is seeking a candidate for a funded two-year postdoctoral position with the possibility of renewal.  The research is sponsored by the Office of Naval Research (ONR) and is subject to availability of funds.  This research position involves:

1.       Conducting research on detection of anomalies and attacks on small-scale power grids also known as microgrids.

2.       Instrumentation and hardware testing on a low voltage, low power research test‐bed microgrid.

3.       Assisting undergraduate independent research projects.


The appointee will be expected to work closely with USNA faculty, students, multi-university research collaborators, experts from Navy Labs and other DOD entities, and private industry. A variety of active projects span academic, government, and industrial research. The academy is a charter member of the Electric Ship Research and Development Consortium ( and heavily involved with various Naval labs and program offices. The supervising faculty have a mix of experience in academia, industry, Navy ship design offices, and as active-duty Naval officers.

This is a non-tenure track federal excepted service position.  The position includes office space, computer facilities, library privileges, conference travel support, health benefits, and a competitive salary.  The position is currently open, and the starting date is negotiable.

Please click the "apply" link to view the official posting with application instructions.



Applicants must have a Ph.D. in electrical engineering, mechanical engineering, control systems engineering, math, computer science, physics or a closely related area.


A background in any of the following areas is beneficial: power systems, networks, sensing, state estimation, control theory, optimization, feedback computing, or autonomic computing. Knowledge of MATLAB/Simulink and rapid prototyping of embedded control systems is also useful.

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