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

UT-Battelle; Oak Ridge (TN); On-site; USD 65,000 - 90,000; Full time

Benefits offered by this job

On-site fitness facilities

Relocation assistance

Educational Assistance

Cafeteria facilities

Job summary

UT-Battelle at Oak Ridge National Laboratory seeks a Postdoctoral Research Associate to advance data-driven modeling, AI, and control strategies for thermal systems, including HVAC and data center cooling.

The role blends thermal-fluid sciences, control theory, and ML to optimize design, operation, and predictive maintenance across system and component levels. Collaboration across groups will be essential to deliver impactful solutions.

Qualifications

• PhD in Computer Science, Computer Engineering, Mechanical Engineering, Electrical Engineering, or related discipline obtained within the last five years.
• Minimum of 3 years of experience in modeling and optimizing complex engineering systems.

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.
• 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.

Skills

Python

MATLAB/Simulink

Machine learning

Control theory

Thermal-fluid knowledge

Education

PhD in CS/CE/ME or related

Tools

TensorFlow

PyTorch

Job description

Overview

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).

Major Duties/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.

Basic Qualifications

• 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

Preferred Qualifications

• 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.

Special Requirements

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

For Foreign National Candidates

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.

For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

About ORNL

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.

Other benefits include the following:

• 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

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.