Home Postdoc Abroad Postdoctoral Appointee -Materials Chemistry, Argonne National Laboratory, USA

Postdoctoral Appointee -Materials Chemistry, Argonne National Laboratory, USA

Postdoctoral Position in USA

Postdoctoral Appointee -Materials Chemistry: Argonne National Laboratory is seeking motivated candidates for the position of Postdoctoral Appointee in the Materials Science Division. This role offers a unique opportunity to conduct cutting-edge research in AI for Materials Chemistry, focusing on energy storage and conversion.

Postdoctoral Appointee – MSD AI for Materials Chemistry

Designation

Postdoctoral Appointee

Location

Lemont, IL, USA

Research Area

  • AI for Materials Chemistry
  • Energy Storage and Conversion

Eligibility/Qualification

Required Qualifications

CriteriaDetails
Educational BackgroundPhD (completed or expected within 0-5 years) in Materials Science, Computational Materials Science, Chemical Engineering, or a related field.
Technical ExpertiseUnderstanding of computational materials science, electronic structure methods, and molecular dynamics.
Programming SkillsProficiency in C++ and/or Python.
Research ContributionsPublications in AI for Materials Chemistry.
Collaboration and CommunicationAbility to work on multiple projects and collaborate with interdisciplinary teams. Strong written and oral communication skills.
Core ValuesCommitment to impact, safety, respect, integrity, and teamwork.

Preferred Qualifications

  • Experience integrating AI techniques with quantum mechanical calculations.
  • Familiarity with advancements in Foundational Models and Agentic AI.

Job Description

The Postdoctoral Appointee will engage in:

  1. Quantum Mechanical Calculations:
  • Performing first-principles or Density Functional Theory (DFT) calculations on molecules/materials and interphases.
  • Using Molecular Dynamics (MD) simulations to study chemical transformations in materials.
  1. Artificial Intelligence Applications:
  • Utilizing machine learning techniques for materials property prediction and Bayesian approaches.
  • Exploring Foundational Models and Agentic AI to solve challenges in energy storage and conversion.

How to Apply

Interested candidates should upload the following documents through Workday:

  • CV/Resume
  • Unofficial Ph.D. Transcripts
  • Ph.D. diploma (if awarded)

Candidates may apply while currently enrolled but must provide proof of degree conferral by the position start date. During the interview, candidates will be asked to provide contact information for three references.

Last Date to Apply

Applications will be accepted until the position is filled. Interested individuals are encouraged to apply promptly.


Join Argonne National Laboratory and contribute to innovative research that is shaping the future of energy storage and conversion!

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