Postdoctoral Researcher – AI-Driven Thermal Systems & Controls

Knoxville Technology Council; Oak Ridge (TN); On-site; USD 65,000 - 85,000; Full time

Job summary

ORNL is seeking a Postdoctoral Research Associate – Modeling, Controls, and AI for Thermal Systems. The role advances data-driven modeling and optimization of HVAC, water heating, and data center cooling, in the Building Equipment Research Group.

Responsibilities include developing physics-based and ML-informed models, validating against experiments, and designing advanced control strategies such as model predictive control and adaptive/ML-based controls, with real-time sensor integration and

Qualifications

• PhD in a related field obtained within the last five years
• 3+ years of modeling/optimizing complex engineering systems
• Experience with thermal-fluid systems and control design
• Experience applying ML to engineering systems
• Proficiency in Python, MATLAB/Simulink, and ML frameworks
• Strong analytical and problem-solving skills

Responsibilities

• Develop physics-based, data-driven, and hybrid models of thermal systems
• Validate models with experimental data and refine accuracy
• Model design, operation, and maintenance scenarios for optimization
• Analyze and improve HVAC, water heating, and data center cooling performance
• Design and implement advanced control strategies (e.g., MPC, adaptive, ML-based)
• Integrate real-time sensor data into closed-loop control and perform testing

Skills

Python programming

ML/AI concepts

Control theory

Analytical thinking

Independent research

Education

PhD in Computer Science/Engineering

Tools

Python

MATLAB/Simulink

TensorFlow/PyTorch

Job description

ORNL is seeking a Postdoctoral Research Associate – Modeling, Controls, and AI for Thermal Systems. The role advances data-driven modeling and optimization of HVAC, water heating, and data center cooling, in the Building Equipment Research Group.

Responsibilities include developing physics-based and ML-informed models, validating against experiments, and designing advanced control strategies such as model predictive control and adaptive/ML-based controls, with real-time sensor integration and