Postdoctoral Appointee - Enhanced statistical methods in computational thermodynamics

This is an opportunity for a knowledgeable and creative individual to be part of a team developing statistical methods for model selection, parameter inference, and uncertainty quantification and propagation in computational thermodynamics. Computational thermodynamics, and especially the CALculation of PHAse Diagrams (CALPHAD) method, is a critical toolset for the discovery and deployment of new materials spanning high-temperature alloys, semiconductors, biological materials, and more. Statistical advancements are urgently needed to accelerate thermodynamic model development and provide uncertainty estimates critical to materials design.

In this role you can expect to:

• Work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories with regular visits to the National Institute of Standards and Technology.
• Design and implement new statistical techniques for computational thermodynamics and to make these techniques available to the community via open-source software development.
• While experience in thermodynamic 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 artificial intelligence, materials engineering, chemistry, and beyond at Argonne National Laboratory.

Position Requirements

Required skills and qualifications:

• A recent or soon-to-be-completed PhD. (typically in the last 0-3 years) in computer science, materials science, chemistry, physics, mathematics, or related engineering disciplines
• Knowledge of statistical techniques including Bayesian methodologies
• 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 model Argonne's core values of impact, safety, respect, integrity, and teamwork

Desirable skills:

• Expertise in physics-based modeling, ideally computational thermodynamics and CALPHAD

Job Family
Postdoctoral Family

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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.

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