Postdoctoral Research Associate - Modeling, Control, and AI for Thermal Systems

Oak Ridge National Laboratory • Oak Ridge, TN

52d • Onsite

About The Position

We are seeking a Postdoctoral Research Associate – Modeling, Controls, and AI for Thermal Systems to advance the design, operation, and maintenance of thermal systems through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance the design and operation of next-generation thermal systems, developing strategies to model, optimize, predict, and autonomously control thermal systems behavior in complex systems. The candidate will contribute to developing and improving the performance of next-generation thermal systems—including HVAC, water heating, and data center cooling—at both the system and component level. This position resides in the Building Equipment Research Group in the Buildings and Transportation Science Division in the Energy Science and Technology Directorate at Oak Ridge National Laboratory (ORNL).

Requirements

• A Ph.D. degree in Computer Science, Computer Engineering, Mechanical Engineering, Electical Engineering, or a related discipline obtained within the last five years.
• A minimum of 3 years of experience in modeling and optimizing complex engineering system is required.
• Demonstrated understanding of thermal-fluid system architecture, including component-level behavior (e.g., heat exchangers, compressors, refrigerant cycles, control valves) and their integration into system-level performance
• Demonstrated experience in control theory and control system design (e.g., model predictive control, feedback control, adaptive control)
• Experience applying machine learning/AI to engineering systems
• Proficiency in programming and simulation tools such as Python, MATLAB/Simulink, and/or ML frameworks (TensorFlow, PyTorch)
• Experience with physics-based modeling
• Strong analytical and problem-solving skills, with the ability to work independently on complex research problems

Nice To Haves

• Strong background in thermal-fluid sciences, heat transfer, or thermal system dynamics
• Prior research experience in thermal systems, controls, or AI/ML applications
• Experience developing and deploying reinforcement learning algorithms for real-time control applications
• Experience with hardware-in-the-loop (HIL) testing or integrating control algorithms into physical testbeds
• Knowledge of predictive maintenance methods and anomaly detection techniques for complex systems
• Experience with sensor networks, IoT platforms, or real-time data acquisition and telemetry systems
• Familiarity with cloud computing platforms and edge deployment of ML models
• Experience with uncertainty quantification, sensitivity analysis, or robust optimization methods
• Track record of publications in relevant journals/conferences (e.g., ASME, IEEE, ASHRAE)
• Excellent written and oral communication skills.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Responsibilities

• Develop physics-based, data-driven, and hybrid (physics-informed ML) models of thermal systems to capture dynamic thermal behavior
• Validate models against experimental data and refine model accuracy and computational efficiency
• Model thermal system design, operation, and maintenance scenarios to support optimization and decision-making
• Analyze and improve performance of HVAC, water heating, and data center cooling systems
• Work across system-level and component-level design challenges
• Integrate predictive and optimization techniques to enhance system efficiency, reliability, and autonomy
• Design and implement advanced control strategies (e.g., model predictive control, adaptive control, feedback/feedforward control, ML-based controls) for thermal system optimization
• Integrate real-time sensor data and telemetry into closed-loop control frameworks
• Test and validate control algorithms through simulation, hardware-in-the-loop testing, and/or physical testbeds
• Apply machine learning techniques to predict thermal system performance and detect anomalies
• Develop AI-driven predictive maintenance strategies to anticipate system failures or performance degradation
• Use AI/ML methods to optimize thermal system design parameters and operating conditions
• 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.

Benefits

• Prescription Drug Plan
• Dental Plan
• Vision Plan
• 401(k) Retirement Plan
• Contributory Pension Plan
• Life Insurance
• Disability Benefits
• Generous Vacation and Holidays
• Parental Leave
• Legal Insurance with Identity Theft Protection
• Employee Assistance Plan
• Flexible Spending Accounts
• Health Savings Accounts
• Wellness Programs
• Educational Assistance
• Relocation Assistance
• Employee Discounts

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree