Postdoctoral Appointee - MSD AI for Materials Chemistry

Argonne National Laboratory • Lemont, IL

2d

About The Position

The Materials Science Division is seeking applicants for a Postdoctoral Appointee who will conduct cutting-edge research in AI for Materials Chemistry, with a focus on energy storage and conversion. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using advanced computational techniques and artificial intelligence. The project involves: 1. Quantum Mechanical Calculations: - Performing first-principles based or Density Functional Theory (DFT) calculations for molecules/materials and interphases - Utilizing Molecular Dynamics (MD) simulations to study chemical transformations in materials. 2. Artificial Intelligence Applications: - Leveraging conventional machine learning techniques for materials property prediction and Bayesian approaches - Exploring Foundational Models and Agentic AI to address challenges in energy storage and conversion.

Requirements

• Educational Background: - A recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Computational Materials Science, Chemical Engineering or a closely related field.
• Technical Expertise: - Comprehensive understanding of applied computational materials science, including electronic structure methods and molecular dynamics. - Experience with High-Performance Computing (HPC) systems and intelligent workflows.
• Programming Skills: - Proficiency in C++/or Python programming languages is essential.
• Research Contributions: - Demonstrated publications in AI for Materials Chemistry.
• Collaboration and Communication: - Willingness to work on multiple projects and collaborate effectively with interdisciplinary teams. - Strong written and oral communication skills.
• Core Values: - Ability to model Argonne’s core values: impact, safety, respect, integrity, and teamwork.

Nice To Haves

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

Responsibilities

• Performing first-principles based or Density Functional Theory (DFT) calculations for molecules/materials and interphases
• Utilizing Molecular Dynamics (MD) simulations to study chemical transformations in materials.
• Leveraging conventional machine learning techniques for materials property prediction and Bayesian approaches
• Exploring Foundational Models and Agentic AI to address challenges in energy storage and conversion.

Benefits

• comprehensive benefits are part of the total rewards package.
• Click here to view Argonne employee benefits!
• As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 1,001-5,000 employees