Oak Ridge National Laboratory

Research Professional - Grid Control and Machine Learning

Oak Ridge National Laboratory$110K — $130K *
Energy & Utilities
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Ph.D. in electrical engineering, power systems, computer engineering, control engineering, or closely related field.
  • Strong background in power system modeling and dynamic simulation.
  • Experience with real-time systems and hardware-in-the-loop simulation.
  • Proficient in programming and data analysis with tools like Python, MATLAB, C/C++, or similar.
  • Ability to develop and document research prototypes and algorithms.
  • Strong communication skills for interdisciplinary collaboration.

Responsibilities

  • Conduct research on real-time measurement-to-model approaches for modern power systems.
  • Develop grid-edge sensing and real-time monitoring systems for high-resolution measurements.
  • Design timing and synchronization architectures for distributed measurements.
  • Integrate grid-edge measurements into workflows for DER aggregation.
  • Apply machine learning for DER model calibration and decision support.
  • Build hardware-in-the-loop test platforms for validation under real-time conditions.
  • Collaborate with engineering teams to transition models into deployable solutions.

Benefits

  • Work within a collaborative and innovative research environment.
  • Engage with a diverse range of clients including government and industry partners.
  • Contribute to projects that enhance grid security and resilience.
  • Opportunities for interdisciplinary research and professional development.
Full Job Description
Requisition Id 16852

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/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.

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.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.



About Oak Ridge National Laboratory

Oak Ridge National Laboratory (ORNL) is a science and technology national laboratory managed for the United States Department of Energy (DOE) by UT-Battelle. ORNL is the largest science and energy national laboratory in the Department of Energy system by size and by annual budget. ORNL conducts research and development activities in a variety of scientific and technical disciplines. ORNL's scientific programs focus on materials, neutron science, energy, high-performance computing, systems biology and national security. ORNL partners with other national laboratories, universities and industry to solve complex problems and transfer knowledge and technology. ORNL is home to several of the world's most powerful supercomputers, including Summit, the world's most powerful supercomputer as of November 2018.
Learn more about Oak Ridge National Laboratory
Size
5,000 employees
Industry
Founded
1943

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