Oak Ridge National Laboratory
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Postdoctoral Research Associate in Modeling, Control, and AI for Thermal Systems Oak Ridge National Laboratory in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Postdoctoral appointment up to 24 months with potential for extension, subject to performance and availability of funding. ORNL offers competitive pay and benefits; relocation assistance and standard employee benefits are mentioned.
Country
United States
University
Oak Ridge National Laboratory
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About this position
Oak Ridge National Laboratory (ORNL) is hiring a Postdoctoral Research Associate in Modeling, Control, and AI for Thermal Systems at its Oak Ridge, Tennessee location in the United States.
The position sits in the Building Equipment Research Group within the Buildings and Transportation Science Division and the Energy Science and Technology Directorate. The research focuses on advancing the design, operation, and maintenance of thermal systems using data-driven modeling, optimization, control theory, and artificial intelligence / machine learning.
Research topics include physics-based, data-driven, and hybrid (physics-informed ML) models; model validation against experimental data; optimization and decision-making for thermal system design and operation; advanced control strategies such as model predictive control, adaptive control, and feedback/feedforward control; real-time sensor data integration; simulation and hardware-in-the-loop testing; predictive maintenance; anomaly detection; and AI/ML methods for improving HVAC, water heating, and data center cooling systems.
Eligibility highlights: applicants must hold a Ph.D. in Computer Science, Computer Engineering, Mechanical Engineering, Electrical Engineering, or a related field, earned within the last five years. The post also requires at least 3 years of experience in modeling and optimizing complex engineering systems, plus strong programming and simulation skills in Python, MATLAB/Simulink, and/or ML frameworks such as TensorFlow or PyTorch.
Preferred background includes thermal-fluid sciences, heat transfer, thermal system dynamics, reinforcement learning, HIL testing, sensor networks, IoT, cloud/edge deployment, uncertainty quantification, robust optimization, and publication experience in ASME, IEEE, or ASHRAE venues.
The appointment is for up to 24 months with possible extension, depending on performance and funding. ORNL notes competitive pay and benefits, including relocation assistance and standard employee benefits.
How to apply: submit your application through the ORNL jobs portal and provide two letters of reference. References may be uploaded directly or emailed to [email protected] with the position title and number in the subject line.
Funding details
Postdoctoral appointment up to 24 months with potential for extension, subject to performance and availability of funding. ORNL offers competitive pay and benefits; relocation assistance and standard employee benefits are mentioned.
What's required
Ph.D. in Computer Science, Computer Engineering, Mechanical Engineering, Electrical Engineering, or a related discipline obtained within the last five years. Applicants must have at least 3 years of experience in modeling and optimizing complex engineering systems, demonstrated understanding of thermal-fluid system architecture, experience in control theory and control system design, experience applying machine learning/AI to engineering systems, proficiency in Python, MATLAB/Simulink, and/or ML frameworks such as TensorFlow or PyTorch, experience with physics-based modeling, and strong analytical/problem-solving skills. Preferred qualifications include thermal-fluid sciences, heat transfer, thermal system dynamics, reinforcement learning, hardware-in-the-loop testing, predictive maintenance, anomaly detection, sensor networks, IoT, cloud/edge deployment, uncertainty quantification, sensitivity analysis, robust optimization, and publications in relevant journals/conferences.
How to apply
Apply through the ORNL jobs portal. Submit your application materials and include two letters of reference; they may be uploaded directly or emailed to [email protected] with the position title and number in the subject line. If you have trouble applying, contact [email protected].
More information can be found here
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