Postdoctoral Position in Applied AI in Electrochemical Device Diagnosis and Prognosis

We are currently seeking a Postdoctoral candidate to join our team in the Data Science and Learning division at Argonne. This is an opportunity for a knowledgeable and creative individual to be part of a team using artificial intelligence (AI) and high-performance computing (HPC) to evaluate the state of health (SOH) of electrochemical energy storage devices (diagnosis) and predict the SOH into the future (prognosis).

The primary projects this postdoc will contribute to relate to lithium-ion batteries, advanced lead-acid batteries, and flow cells with applications including long duration energy storage and electrified aviation. Each project will involve close collaboration with domain experts to leverage emerging computing techniques to solve pressing challenges in energy storage.

Primary responsibilities will be to design and implement new techniques for the diagnosis and prognosis of electrochemical energy storage systems. While experience in electrochemical modeling is a benefit, ideal candidates will be expected to work together with domain experts rather than possess all required expertise themselves. Beyond the listed projects, the candidate will be able to contribute to other large-team scientific projects in materials engineering, chemistry, and beyond at Argonne National Laboratory.

Position Requirements

Required skills and qualifications: Completed PhD (typically within the last 0-3 years) 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 Good scientific productivity, as demonstrated by publications and conference presentations.

Preferred skills, however not required:

• Expertise in physics-based modeling, ideally electrochemical modeling Effective oral and written communication skills.
• Experience with analyzing large and/or complex data sets

Job Family Postdoctoral Family Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time

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