Research Professional - Grid Control and Machine Learning
Listed on 2026-08-02
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Engineering
Systems Engineer, Electrical Engineering, Software Engineer, Robotics
Overview:
The Grid Interactive Controls Research Group (GIC) in the Electrification and Energy Infrastructure Division (EEID) within the Energy Science and Technology Directorate (ESTD) at Oak Ridge National Laboratory (ORNL) is seeking a R&D associate staff member. The GIC Group aims to improve grid security, reliability and resilience through everything-to-grid (X2G) integration by delivering innovative, multi-disciplinary, grid-interactive control solutions. The successful candidate will work with a wide variety of customers including the US Department of Energy.
They may also work collaboratively with other national laboratories, industry and academic partners, and the international community to execute projects. The successful candidate is expected to demonstrate a broad understanding and wide application of engineering principles, theories, and concepts as well as general knowledge of power systems-related disciplines, applications and challenges.
Specifically, the successful candidate will focus on research and development of grid-edge sensing, reliable timing, model aggregation, and hardware-in-the-loop validation for modern power systems. The objective is to improve dynamic observability, model fidelity, and operational resilience in increasingly distributed and converter-dominated power grids.
Major Duties/Responsibilities:- Conduct innovative research on real-time, time-synchronized measurement-to-model approaches for modern power systems with high DER penetration.
- Develop grid-edge sensing and real-time monitoring systems to provide high-resolution measurements for situational awareness, dynamic modeling, and control decisions.
- Design reliable timing and synchronization architectures to ensure distributed grid-edge and substation measurements are time consistent and model ready.
- Integrate grid-edge measurements and system-level observations into unified workflows for DER aggregation and parameter identification.
- Apply machine learning techniques to improve DER/I model calibration, parameter estimation, uncertainty assessment, and decision support.
- Build and apply hardware-in-the-loop test platforms to validate the full sensing, timing, modeling, and control under real-time conditions.
- Establish HIL-based test protocols and performance metrics to assess accuracy, latency, and robustness under practical engineering constraints.
- Collaborate with power systems, controls, hardware, and field engineering teams to transition measurement-driven models and real-time prototypes into deployable, control-ready solutions.
- Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace - in how we treat one another, work together, and measure success.
- Ph.D. in electrical engineering, power systems, computer engineering, control engineering, or a closely related field.
- Strong background in power system modeling, dynamic simulation, and DER integration.
- Experience with real-time systems, hardware-in-the-loop simulation, or power system testbeds.
- Proficiency in programming and data analysis using tools such as Python, MATLAB, C/C++, or similar languages.
- Ability to develop, validate, and document research prototypes, algorithms, and technical workflows.
- Strong written and verbal communication skills for interdisciplinary research and engineering collaboration.
- Experience with grid-edge sensing, synchronized measurements, PMU/POW data, or distribution-level monitoring systems.
- Experience with reliable timing, time synchronization or timing-error impact analysis.
- Experience with DER model aggregation, parameter identification, model reduction, or dynamic equivalent modeling.
- Experience with power system simulation and modeling tools such as PSCAD, PSSE, OpenDSS, MATLAB/Simulink, or similar platforms.
- Hands‑on experience with real-time simulation or hardware-in-the-loop platforms such as OPAL‑RT, RTDS, Typhoon HIL, or similar systems.
- Familiarity with software coding and hardware development in the context of power systems…
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