Agentic AI and Multiscale Atomistic-to- Continuum Modeling Postdoctoral Research Associate

XTD-PRI and T-1 are seeking an outstanding postdoctoral researcher to advance agentic AI for scientific discovery and multiscale materials modeling. The position combines the development of AI-driven scientific-computing frameworks with physics-based research connecting atomistic simulations to continuum-scale descriptions of materials under extreme conditions.

The successful candidate will join a multidisciplinary team spanning artificial intelligence, computational materials science, atomistic simulation, theoretical physics, and continuum multiphysics modeling. The central research objective will be to develop atomistic simulation approaches capable of reproducing critical experimental observables and develop continuum models from these simulations. AI will play a key role, both in running the atomistic simulations and developing the scale bridging models.

This work will investigate both autonomous and human-in-the-loop approaches, with an emphasis on physical reasoning, scientific validation, reproducibility, and appropriate researcher oversight.

Critically, these approaches will need to work across wide ranges of materials science, including organic and soft matter system as well as metals and materials. Research may include constitutive descriptions, reduced-order models, coarse-graining strategies, uncertainty quantification, and methods for transferring information across spatial and temporal scales. For example, an agentic workflow might identify a gap in a continuum material model, propose targeted atomistic calculations, evaluate the resulting evidence, and support iterative model refinement.

The position offers opportunities to advance both the underlying materials physics and the methods by which AI agents conduct reliable computational research.

You will have opportunities to:

• Develop the skills needed to become a next-generation scientist using agentic AI. Gain hands‑on experience framing scientific questions, directing AI agents, evaluating their work, recognizing limitations, independently verify results and managing AI‑assisted research. Help define the practical skills, training methods, and research practices that prepare the scientific workforce to use agentic AI effectively and responsibly.
• Build experience in both atomistic and continuum simulations of chemistry and materials science. This will include a range of simulations including electronic structure theory and fluid dynamics, as well as simulations at intermediary length scales.
• Develop scientific‑computing frameworks that connect AI models, atomistic simulations, continuum models, scientific knowledge, analysis tools, and high-performance computing resources.

The position provides substantial opportunity to define new research directions of agentic AI, atomistic materials modeling and continuum computational physics.

• Experience developing, integrating, or evaluating AI agents, tool‑using language‑model systems, or AI‑driven research workflows. Relevant experience may include research planning, tool integration, multi‑agent coordination, automated analysis, or deployment in scientific‑computing environments. Experience with Codex, Claude, or comparable systems is relevant; expertise in a particular platform is not required.

• Demonstrated experience with computational modeling of materials using electronic structure theory, molecular dynamics, coarse graining, continuum modeling, or closely related computational methods.