Post-Doctoral Associate - Machine Intelligence for Energy Storage and Grid Applications

Requisition Id 12699 

Purpose:

We are seeking a post-doctoral associate to advance science and technology in the areas of machine vision, data science and analytics, and diagnostics for energy storage systems in transportation and grid sectors for second life applications among other battery R&D duties.  Selection will be based on qualifications, relevant experience, technical skills, and education. The chosen candidate is expected to develop and execute research projects on applying advanced machine learning algorithms to applications of second life use of electrochemical energy storage systems.

 

Candidates should have demonstrated record of technical accomplishments in 1) advanced machine learning algorithms including but not limited to convolutional neural networks, deep learning, variational auto encoders, segmentation and feature identification, etc.; 2) ability to data-wrangle large data-sets that may include images, video, 3D cad files, time series, electrochemical data-sets and 3) prior knowledge on fundamental principles of electrochemical systems (viz. batteries), machine design (CAD, 3D parts), as well as power electronics. Demonstrated ability to build data-utility pipelines for high throughput analysis will be favorable. These research areas are aligned with U.S. Department of Energy (DOE) Office of Vehicle Technologies and other DOE programs.

 

This postdoctoral position is in the Energy Systems Integration Division, and research will primarily be conducted at ORNL’s Grid Research, Integration & Deployment Center (GRID-C). The successful candidate will collaborate with staff across ORNL and will be expected to communicate at conferences and project review meetings and interface with scientists at other national labs, industry, and universities.

 

Major Duties / Responsibilities:

• Conduct works on machine vision to aid disassembly of end-of-life battery packs
• Develop new diagnostic routines for rapid identification of the state of health of different energy storage systems
• Develop machine learning algorithms for real-time detection of the status of energy storage systems
• Conduct work on thorough electrochemical testing of battery systems
• Utilize advanced active learning techniques to efficiently reduce the need for exhaustive experimental testing.
• Design and implement data science pipelines for the creation and maintenance of databases, ensuring efficient tracking and analysis of extensive experimental data.
• Write, develop, and review technical products such as proposals, publications, and artifacts to ensure high quality and consistency with ORNL standards
• Ensure safe, collegial, and collaborative workplace
• Network and develop collaboration with other groups and divisions internally at ORNL and with other national labs and industry
• Ensure safe, collegial, and collaborative workplace
• 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:

• A Ph.D. in Materials Science, Chemistry, or related disciplines with a strong focus on machine learning applications to domain science
• A record of excellence in battery research and development as evidenced through peer-reviewed journal articles and/or issued patents.
• Excellent written and oral communication skills.

 

Preferred Qualifications:

• 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.

 

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 three letters of reference when applying to this position. You may upload these directly to your application or have them sent to recruiting@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,