Research Professional - Grid Control and Machine Learning

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

Select how often (in days) to receive an alert:

Research Professional - Grid Control and Machine Learning

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/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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Basic 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.

Preferred Qualifications:

• 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 research.
• Proven track record of scholarly publications and presentations in relevant fields.
• Experience in proposal writing.

Special Requirements:

Security, Credentialing, and Eligibility Requirements:For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.

Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.