Digital Twin Intern
Southern Arkansas University, Idaho Falls, ID, United States
⚛️
Advance the Future of Nuclear Energy with INL!
Idaho National Laboratory (INL) is seeking a
graduate student in Nuclear Engineering (or a closely related field)
for a
Summer 2026 internship
to help develop a
discrepancy checking and diagnosis tool
for a
digital‑twin–based supervisory control system . This is a hands‑on research opportunity to apply
AI and machine learning
methods to next‑generation energy systems.
Responsibilities
Integrate
active learning ,
reinforcement learning , and
multi‑agent AI systems
into a digital twin (DT) framework.
Design and implement methods for
discrepancy detection and diagnosis
within a DT‑based supervisory control loop.
Apply
verification, validation, and uncertainty quantification (VVUQ)
to evaluate model fidelity and decision robustness.
Analyze simulation and sensor data, develop ML models in
Python , and contribute to
technical reports and publications .
Enrolled full time in a
Ph.D. program
in
Nuclear Engineering
or a closely related field.
Coursework or experience in
modeling & simulation ,
machine learning ,
verification & validation , and
uncertainty quantification .
Familiarity with
Python
and strong analytical skills.
Minimum
3.0 GPA
and authorization to work in the U.S. (including CPT/OPT).
Preferred Qualifications
Experience with
reinforcement learning ,
active learning ,
digital twins , or
advanced control systems .
Comfort with
time‑series/sensor data
and
data–model discrepancy analysis .
Excellent written and verbal communication skills and a passion for collaborative research.
Onsite at INL – Idaho Falls, ID
️
Summer 2026 | Flexible start date
9x80 schedule (every other Friday off)
Doctoral-level internship | Course credit may be available
Join us and help shape the future of
AI‑driven nuclear control systems
through cutting‑edge digital twin research.
#J-18808-Ljbffr
Advance the Future of Nuclear Energy with INL!
Idaho National Laboratory (INL) is seeking a
graduate student in Nuclear Engineering (or a closely related field)
for a
Summer 2026 internship
to help develop a
discrepancy checking and diagnosis tool
for a
digital‑twin–based supervisory control system . This is a hands‑on research opportunity to apply
AI and machine learning
methods to next‑generation energy systems.
Responsibilities
Integrate
active learning ,
reinforcement learning , and
multi‑agent AI systems
into a digital twin (DT) framework.
Design and implement methods for
discrepancy detection and diagnosis
within a DT‑based supervisory control loop.
Apply
verification, validation, and uncertainty quantification (VVUQ)
to evaluate model fidelity and decision robustness.
Analyze simulation and sensor data, develop ML models in
Python , and contribute to
technical reports and publications .
Enrolled full time in a
Ph.D. program
in
Nuclear Engineering
or a closely related field.
Coursework or experience in
modeling & simulation ,
machine learning ,
verification & validation , and
uncertainty quantification .
Familiarity with
Python
and strong analytical skills.
Minimum
3.0 GPA
and authorization to work in the U.S. (including CPT/OPT).
Preferred Qualifications
Experience with
reinforcement learning ,
active learning ,
digital twins , or
advanced control systems .
Comfort with
time‑series/sensor data
and
data–model discrepancy analysis .
Excellent written and verbal communication skills and a passion for collaborative research.
Onsite at INL – Idaho Falls, ID
️
Summer 2026 | Flexible start date
9x80 schedule (every other Friday off)
Doctoral-level internship | Course credit may be available
Join us and help shape the future of
AI‑driven nuclear control systems
through cutting‑edge digital twin research.
#J-18808-Ljbffr