This is an opportunity for a knowledgeable and creative individual to be part of a team using artificial intelligence and high-performance computing to enable autonomous coating manufacturing to support U.S. domestic manufacturing in renewable energy industry, such as fuel cell, hydrogen production, and lithium battery. The scientific goals are to explore the relationship between ink property, coating process, membrane microstructure, and electrode performance, and to use physics-informed artificial intelligence (AI) and edge sensors to improve coating process precision and reliability.

The successful candidate will work closely with researchers at the Applied Materials Division and the Data Science and Learning division of Argonne National Laboratory. The primary responsibilities of the post-doc candidate will be to apply machine learning techniques to achieve defect identification from image and spectrum data and to develop hierarchical learning framework that enables autonomous coating process control. A benefit, ideal candidate will be expected to work closely with domain experts within a multidisciplinary team to leverage emerging computing techniques to solve pressing challenges. Beyond the listed technical development, the candidate is expected to contribute to project reporting and scientific proposal preparation, as well as to present Argonne’s research at peer-reviewed journals and domestic and international conferences.

Position Requirements

• PhD. in computer science, materials science, chemistry, physics, mathematics, or related engineering disciplines.

• Knowledge of deep learning techniques for time-series and image data.

• Experience with applying machine learning or other elements of artificial intelligence to solving significant scientific or engineering problems.

• Interest in software development, with particular emphasis on the Python programming language and contributions to open-source scientific software.

• Demonstrated scientific productivity, as demonstrated by publications and conference presentations.

• A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. 

Preferred Skills:

• Experience in slurry synthesis and rheologic property characterization.

• Experience in wet film deposition and drying.

• Experience with analyzing large and/or complex datasets.

• Knowledge in physics-based modeling such as fluid dynamics or Multiphysics.

• Effective oral and written communication skills.

Job Family

Job Profile

Worker Type

Time Type

As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.  

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis.  Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements.  Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.