Postdoctoral Research Associate - Electric Vehicle Supply Equipment

Requisition Id 14037 

Overview:  

The Electrical Drives Research Group in Buildings and Transportation Science Division at the Oak Ridge National Laboratory (ORNL) is seeking applicants for a post-doctoral R&D Associate position to conduct research on Artificial Intelligence and Machine Learning (AI&ML) applications to EVSE diagnostics and prognosis.The successful candidate will be capable of developing ML models, performing modeling, simulating, designing and optimizing algorithms for successful implementation of preventive diagnostics and prognosis of EVSE.

 

As a U.S. Department of Energy (DOE) Office of Science National Laboratory, ORNL has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 6,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation.

 

Major Duties / Responsibilities

• Conduct R&D projects focused on the application of AI and ML techniques to develop preventive diagnostics and prognosis algorithms for EVSE sub-systems ensuring high reliability and reducing no-charge events.
• Develop simulation models and prototype of EVSEs.
• Develop advanced mathematical frameworks for diagnostics and prognosis models, integrating signal processing techniques (e.g., wavelets, Hilbert Transforms) to enhance model accuracy and predictive capability.
• Translate frameworks into detailed software architecture using modeling languages like SysML and work closely with software engineering teams to implement these architectures into scalable and efficient diagnostic algorithms in Python.
• Extensive validation and testing of diagnostic and prognosis algorithms, ensuring robustness and reliability in real-world applications, and conducting rigorous performance assessments.
• Integrate AI/ML techniques with signal processing methods to develop robust, real-time diagnostics and prognosis models for EV traction systems, ensuring cutting-edge fault detection capabilities.
• Contribute to the expansion of R&D efforts by proactively identifying new AI/ML applications in EV drivetrain diagnostics.
• Present research findings and technical innovations at prominent conferences (IEEE, SAE) and meetings, while preparing high-quality reports and publications to communicate research outcomes effectively to sponsors, industry partners, and peers.
• Travel as necessary to collaborate with external partners, present research findings, and engage with industry leaders to promote the adoption and commercialization of advanced diagnostic technologies.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success.

 

Basic Qualifications:

• PhD in electrical engineering with a focus on EV chargers and power electronics or controls completed within the last five years.

 

Preferred Qualifications:

• Sound understanding EV charger ecosystem.
• Experience with AI/ML techniques and their application in preventive diagnostics and prognosis for EVSEs and power electronics systems.
• Proficiency in software architecture development and modeling tools such as SysML, as well as strong Python programming skills, with a proven ability to implement complex diagnostic algorithms.
• Familiarity with signal processing techniques, such as wavelets, Fourier transforms, and Hilbert transforms, and their integration into diagnostics and prognosis algorithms for fault detection.
• Experience with extensive validation and testing of algorithms, ensuring robustness, accuracy, and relia