Postdoctoral Appointee - MSD AI for Materials Chemistry

Requirements

• Experience in integrating AI techniques with quantum mechanical calculations.
• Familiarity with recent advancements in Foundational Models and Agentic AI.
• Please upload the following as attachments to your application via Workday:
• CV/Resume
• Unofficial Ph.D. Transcripts
• If already awarded, a copy of your Ph.D. diploma. Candidates can be currently enrolled to apply, but must have proof of degree conferral by the position start date.
• At the point of interview, candidates will be asked to submit the name/contact of three references.
• Job Family
• Postdoctoral
• Job Profile
• Postdoctoral Appointee
• Worker Type
• Long-Term (Fixed Term)
• Time Type
• Full time
• The expected hiring range for this position is $72,879.00-$121,465.00.
• Click here to view Argonne employee benefits!

Benefits

Additional Information

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. Position Requirements Candidates must meet the following qualifications: 1. 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. 2. 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. 3. Programming Skills: - Proficiency in C++/or Python programming languages is essential. 4. Research Contributions: - Demonstrated publications in AI for Materials Chemistry. 5. Collaboration and Communication: - Willingness to work on multiple projects and collaborate effectively with interdisciplinary teams. - Strong written and oral communication skills. 6. Core Values: - Ability to model Argonne's core values: impact, safety, respect, integrity, and teamwork.

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