Grid Control & ML Researcher — Real-Time Power Systems

Oak Ridge National Laboratory; Oak Ridge (TN); On-site; USD 80,000 - 110,000; Full time

Job summary

Oak Ridge National Laboratory (ORNL) invites applications for a Research Professional - Grid Control and Machine Learning. The role focuses on grid-edge sensing, timing, model aggregation, and hardware-in-the-loop validation to improve observability and resilience in distributed power grids.

You will develop real-time measurement-to-model workflows, apply machine learning for DER calibration, and collaborate with power systems and hardware teams to deploy deployable control-ready solutions.

Qualifications

• 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.

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/IBR 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.

Skills

Python programming

MATLAB

C/C++

Data analysis

Technical communication

Education

Ph.D. in electrical engineering or closely related field

Tools

PSCAD

PSSE

OpenDSS

MATLAB/Simulink

OPAL-RT

RTDS

Typhoon HIL

Job description

Oak Ridge National Laboratory (ORNL) invites applications for a Research Professional - Grid Control and Machine Learning. The role focuses on grid-edge sensing, timing, model aggregation, and hardware-in-the-loop validation to improve observability and resilience in distributed power grids.

You will develop real-time measurement-to-model workflows, apply machine learning for DER calibration, and collaborate with power systems and hardware teams to deploy deployable control-ready solutions.