Organization

National Energy Technology Laboratory (NETL)

Reference Code

NETL-FRP-2024-Siriwardane

How To Apply

A complete application consists of:

• An application, including academic history, work history experiences, and honors/awards
• A current resume/CV, including academic history, employment history, relevant experiences, and publication list
• Two educational or professional recommendations You must provide contact information for at least two recommenders in your application. The first two recommendations received will be attached to your application for review by NETL. You may click the "send" (paper airplane) button to send the recommendation request email immediately after entering their information prior to submitting your application; if not, a request will automatically be sent when you submit your application. Your recommenders will receive an email with a subject line of "[Your Name] - ORISE Recommendation Request - [your email]", from [email protected]. This email will include information on the opportunity to which you have applied, as well as a secure link to submit a recommendation for you for this application. If you ask the same person to submit a recommendation for you for multiple applications in Zintellect, they must click the unique link in each email request, but will be given the opportunity to copy over what they had previously submitted.

All documents must be in English or include an official English translation.

If you have questions about the application process, contact [email protected].

After you have submitted an application in Zintellect, you may reach out to [email protected] to request to talk with the hosting researcher if you would like additional information on the project or to express particular interest. You must have a completed application in Zintellect to receive a response.

Selection Decisions

Selection decisions are made directly by NETL researchers and staff looking to host an internship. Your application will be available to the hosting mentor(s) for up to 12 months after you apply. You may withdraw your application at any time. Applications may be reviewed and selected on a rolling basis or the hosting mentor(s) may choose to wait until after the application deadline before reviewing all applications simultaneously. A final decision of non-selection may not be confirmed for several months after the listed application deadline.

Application Deadline

6/30/2026 11:59:00 PM Eastern Time Zone

Description

The National Energy Technology Laboratory's (NETL's) record of success has been built on understanding the future of energy and the technologies required to make that future possible. We’ve long touted our success in developing the technologies that took on acid rain in the 1970s and mercury in the early 2000s. More recently, NETL has a leading role in President Biden’s ambitious climate goals, including a carbon emission-free power sector by 2035 and a net-zero economy by 2050.

Program Goals

The NETL Faculty Research Program (FRP) offers qualified academic faculty an opportunity to collaborate with NETL principal investigators on research that is mutually beneficial to NETL and the selected applicant at state-of-the-art NETL facilities. While typical appointments are part-time, some appointments are offered on a full-time basis during the summer or as a sabbatical. Prior to the appointment, the NETL principal investigator and selected applicant will define the scope of research and schedule the appointment period. Appointment periods range from one month to more than one year. Funding varies and is awarded based upon the participant's institutional salary. Faculty members are expected to elevate the collaboration with NETL by supporting connections with students at their home institution, in addition to the research project.

Connecting Students with NETL

The Collaboration Between The Selected Faculty Member And NETL Will Include Connecting Their Academic Institution And Students With NETL. Student Connections May Be Fostered Through Activities Such As, But Not Limited To, The Following

• Inviting NETL scientists and engineers to present at a departmental seminar
• Joining NETL at institutional career/job fairs to discuss your experiences with NETL
• Speaking about your experiences with NETL at information sessions
• Sharing invitations to NETL information sessions with students
• Partnering with NETL on proposals and other funding opportunities
• Recommending opportunities to NETL scientists and engineers, such as serving as a reviewer or editor, leading a workshop, etc.
• Serving as an ambassador to NETL for students interested in careers in the national lab complex

Research Project

Through the Oak Ridge Institute for Science and Education (ORISE), this posting seeks a faculty collaborator to engage in projects with the Research Innovation Center (RIC) at the National Energy Technology Laboratory (NETL) in the area of Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications (SMART) under the mentorship of Hema Siriwardane. This project will be hosted by the NETL Morgantown, WV campus.

The SMART Initiative is a multi-organizational effort that is transforming the ability to make better, informed decisions related to the subsurface through real-time visualization, forecasting, and virtual learning. The outcomes of the SMART Initiative are science-informed ML-based tools that can be applied at carbon storage sites throughout the nation and the world to: (1) improve the ability to consolidate technical knowledge, site-specific characterization information, and real-time data in a digestible way; (2) enable the optimization of carbon storage reservoirs by creating a capability for “real-time” forecasting of carbon storage reservoir behavior; and (3) improve the ability to understand and communicate subsurface behavior during carbon storage operations to non-experts. This project will support the objectives of the SMART initiative, specifically in making improvements to tools developed under SMART or in developing complementary tools based on Machine Learning for subsurface applications in support of Geologic Storage of CO2.

The participant will learn about the research performed under the SMART initiative and collaborate on making improvements to tools developed under SMART and research complementary tools based on Machine Learning for subsurface applications in support of Geologic Storage of CO2.

Stipend: The selected faculty participant will receive a monthly stipend commensurate with their institutional salary.

Deliverables: To document the effectiveness of the program, participants are required to submit a pre-appointment and post-appointment survey, as well as a reflection on their appointment experience when they renew or end their appointment. The reflection should summarize their project(s), additional activities, and overall experience. Details are provided as the appointment end date approaches.

Participants may also have the opportunity to contribute to manuscripts, journal articles, book chapters, conference presentations, posters, patents, and other publications as a part of their appointment. Such achievements should also be reported to ORISE; additional details are provided after an offer has been accepted.

The National Energy Technology Laboratory (NETL), part of the U.S. Department of Energy (DOE) national laboratory system, is owned and operated by the DOE. NETL supports the DOE mission to advance the energy security of the United States. This is an educational opportunity offered by NETL and administered by the Oak Ridge Institute for Science and Education. Participants in the program are not considered employees of NETL, DOE, the program administrator, or any other office or agency.

Qualifications

To be eligible, applicants must be a full-time regular permanent faculty member at an accredited college/university with a research interest in NETL core R&D areas.

The Ideal Candidate Would Have

• PhD degree in Engineering, Mathematics, Geological Sciences, or a related field
• Experience in science-informed machine learning (i.e., physics-informed machine learning) related to Geologic Storage of CO2
• Experience in applications of machine learning related to Geologic Storage of CO2

Point of Contact

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Eligibility Requirements">12 )

• Citizenship: LPR or U.S. Citizen

• Degree: Master's Degree or Doctoral Degree.

• Discipline(s):

  • Chemistry and Materials Sciences (<a href="#" data-bs-toggle="popover" tabindex="0" role="button" data-bs-title="Chemistry and Materials Sciences" data-bs-content="

    • Analytical Chemistry
    • Bio-inorganic Chemistry
    • Bio-organic Chemistry
    • Biophysical Chemistry
    • Chemistry (General)
    • Environmental Chemistry
    • Inorganic Chemistry
    • Materials Sciences
    • Organic Chemistry
    • Physical Chemistry
    • Polymer Chemistry
    • Theoretical Chemistry

  • Communications and Graphics Design (2 )

    • Computer, Information, and Data Sciences (17 )

      • Earth and Geosciences (21 )

        • Engineering (27 )

          • Environmental and Marine Sciences (14 )

            • Life Health and Medical Sciences (51 )

              • Mathematics and Statistics (11 )

                • Physics (16 )

                  • Science & Engineering-related (2 )

                    • Social and Behavioral Sciences (29 )

                      • Age: Must be 18 years of age

                      Affirmation

                      I Certify That

                      • To the best of my knowledge all information contained in this application is accurate.
                      • I am currently a faculty member at an accredited college/university.
                      • I understand that any falsification will render me ineligible for participation and, if found after participation has begun, may require me to reimburse any funds received.