Machine Learning Subsurface Characterization to Geologic Storage

Position: Machine Learning Applications for Subsurface Characterization to Support Geologic Storage of CO2

Overview

Machine Learning Applications for Subsurface Characterization to Support Geologic Storage of CO2

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. The email communication for recommendations will originate from Zintellect. All documents must be in English or include an official English translation.

Questions about the application process: NETLinfo. After submission, you may contact internship.program.gov to request information on the project or to express interest. A completed application in Zintellect is required to receive a response.

Selection Decisions

Selection decisions are made by NETL researchers and staff hosting internships. Applications may be reviewed on a rolling basis or after the Final date to receive applications. A final decision of non-selection may take several months after the deadline.

Final date to receive applications

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

Description

NETL’s mission includes advancing energy technologies to meet climate goals. The NETL Faculty Research Program (FRP) offers qualified academic faculty an opportunity to collaborate with NETL principal investigators at state‑of‑the‑art facilities. Appointments are typically part-time; some may be full-time during summer or sabbatical.

Program Goals

FRP enables collaboration between NETL principal investigators and selected applicants. The appointment period is defined before start and can range from one month to more than one year. Funding varies and is based on the participant’s institutional salary. Faculty members are encouraged to connect with students at their home institution.

Connecting Students with NETL

• Invite NETL scientists to departmental seminars
• Participate in institutional career/job fairs with NETL
• Share NETL information sessions with students
• Collaborate on proposals and funding opportunities
• Recommend NETL opportunities to NETL staff
• Act as an ambassador to NETL for students

Research Project

The posting seeks a faculty collaborator to engage with the Research Innovation Center (RIC) at NETL Morgantown, WV, in the area of Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications (SMART) under the mentorship of Hema Siriwardane. The project is hosted by NETL Morgantown.

The SMART Initiative aims to improve real-time visualization, forecasting, and learning for subsurface decisions, supporting carbon storage site optimization and communication of subsurface behavior to non-experts. The participant will learn about SMART and contribute to tools and ML-based methods for subsurface applications in support of Geologic Storage of CO2.

Stipend: Monthly stipend commensurate with institutional salary.

Deliverables: Pre- and post-appointment surveys and a reflective summary. Opportunity to contribute to manuscripts, journal articles, conference presentations, posters, patents, and other publications, reported to ORISE.

Participants are not considered NETL or DOE employees. This is an educational opportunity administered by ORISE.

Qualifications

Eligible applicants must be full-time regular faculty at an accredited college/university with research interests aligned to NETL core R&D areas.

The Ideal Candidate Would Have

• PhD in Engineering, Mathematics, Geological Sciences, or related field
• Experience in science-informed (physics-informed) ML related to Geologic Storage of CO2
• Experience applying ML to Geologic Storage of CO2

Point of Contact

Cinnamon

Eligibility Requirements

Citizenship: LPR or U.S. Citizen. Degree:
Master’s or Doctoral. Disciplines and related fields may include Chemistry, Materials Sciences, Environmental Chemistry, Inorganic/Organic/Physical Chemistry, Geosciences, Engineering, Mathematics and Statistics, Physics, and related science and engineering fields. Must be at least 18 years of age.

Affirmation

I certify that all information in this application is accurate, I am currently a faculty member at an accredited college/university, and I understand that falsification may render me ineligible or require reimbursement of funds if discovered after participation has begun.

Notes:
For applicants and program details, refer to the original posting. This refined description preserves the essential content while complying with formatting guidelines.

Seniority level

• Internship

Employment type

• Internship

Job function

• Engineering and Information Technology

Industries

• Government Administration

#J-18808-Ljbffr