Postdoctoral Research Associate - Modeling, Control, and AI for Thermal Systems
要求 Id 17135 **概述：** 我们正在寻求博士后研究员，用于通过数据驱动的建模和优化来推进热系统的设计，运行和维护。成功的候选人将在热液科学，控制理论和人工智能/机器学习交叉点工作，以推进下一代热系统的設計和操作，开发模型，优化，预测和自主控制热系统行为在复杂系统中的策略。候选人将为系统和组件层面的下一代热系统，包括 HVAC,水供暖和数据中心冷却开发和提高性能做出贡献。该职位位于[奥克里奇国家实验室]能源科学与技术总局建筑运输科学部门的建筑设备研究小组 (Building Equipment Research Group) ଠାରେ. **主要责任：** - 发展基于物理，数据驱动和混合式 （物理学信息 ML）的热系统模型以捕捉动态热行为 - 验证模型与实验数据相比，完善模型精度和计算效率 - 支持优化和决策的模特热系统设计，运行和维护场景 - 分析并提高 HVAC,水供暖和数据中心冷却系统的性能 - 在系统层面和组件层面的设计挑战中工作 - 将预测和优化技术整合起来以增强系统效率，可靠性和自主权力 - 为热系统优化设计和实施先进控制战略 （例如模型预测控制，适应控制，反/传送控制，ML 基础上的控制）- 集成实时传感器数据和遥测到闭环控制框架中-通过模拟，硬件循环测试和/或物理测试台测试并验证控制算法-应用机器学习技术来预测热系统性能和检测异常情况-开发基于人工智能的预测维护策略以预测系统故障或性能下降-使用 AI/ML 方法优化热系统设计参数和运营条件-通过与我们的核心价值观影响力，诚信性，团队合作，安全和服务进行协调行为，优先事项和互动实现 ORNL 任务。促进
查看英文原文
Requisition Id 17135
**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](https://www.ornl.gov/) (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.**
Please submit two letters of reference when applying to this position. You may upload these directly to your application or have them sent to [Postdocrecruitment@ornl.gov](mailto:Postdocrecruitment@ornl.gov) with the position title and number referenced in the subject line.
Instructions to upload documents to your candidate profile:
- Login to your account via jobs.ornl.gov
- View Profile
- Under the My Documents section, select Add a Document
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.
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.
**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, and 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.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.
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.