This is an opportunity for a knowledgeable and creative individual to be part of a team using artificial intelligence and high-performance computing 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.
The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. 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:
- Recently completed PhD (within the last 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
- Effective oral and written communication skills
- Ability to collaborate with domain subject matter experts professionally and productively
- Ability to model Argonne's core values of impact, respect, safety, integrity, and teamwork.
Desirable skills:
- 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
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full timeThe expected hiring range for this position is $70,758.00-$117,925.00.
Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
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