Postdoc - Theory-in--loop of Autonomous Experiments Materials--Design

Select how often (in days) to receive an alert:

Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security.

We are seeking an outstanding Postdoctoral Research Associate with a strong background in condensed‑matter physics and materials science and expertise in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight‑binding, machine learning force fields, phase‑field modeling, and/or kinetic Monte Carlo), as well as experience in developing and/or applying advanced AI/ML methods to accelerate materials discovery. The project will involve integrating such theory‑informed AI‑models for creating integrated autonomous experimental synthesis and characterization cross‑facility agentic‑AI platforms that allow real‑time guidance and control of these multi‑modal experiments for targeted discovery of novel quantum and/or microelectronic materials, enabling Labs‑of‑the‑Future (LoTF) for breakthrough science.

The position resides in the Nanomaterials Theory Institute (NTI) within the Theory and Computation Section (TACS) at the Center for Nanophase Materials Sciences (CNMS) Division, Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL) and will also have opportunities to collaborate with the Multiscale Dynamics and Heterogeneities in Quantum Materials themes at the CNMS and US DOE’s Genesis projects.

The candidate is expected to work closely with Soumendu Bagchi and P. Ganesh.

As part of our research team, you will be working with a highly interdisciplinary team of scientists at the CNMS, and across other divisions at ORNL.

• Work closely with members of NTI and CNMS to develop HPC workflows that can perform multi‑fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques
• Develop physics‑informed AI/ML surrogate models for inverse design of new materials and processes, incorporating simulated and experimental multi‑modal datasets.
• Develop AI/ML approaches to bridge length‑ and time‑scales in simulations.
• Design, develop, and validate physics‑informed AI/ML models with features from electronic structure, spectroscopy to control materials growth and emerging functionalities.
• Develop and train agentic AI tools that can control simulation and/or experiments.
• Present and report research results and publish in peer‑reviewed journals in a timely manner.
• Ensure compliance with environment, safety, health, and quality program requirements.
• Maintain a strong commitment to the implementation and perpetuation of values and ethics.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service.

• A PhD in Condensed‑Matter Physics, Materials Science, Chemistry, Physics, or a closely related science discipline completed within the last five years.

• A demonstrated record of developing advanced physics‑informed AI models for scientific discovery.
• Hands‑on expertise developing and applying machine learning for materials and/or process discovery, particularly quantum and/or microelectronic materials.
• Expertise in using or developing agentic tools for automation of scientific discovery.
• Expertise in using high‑performance computing (HPC) platforms for delivering breakthrough scientific results.
• A record of productive and creative research proven by publications in peer‑reviewed journals and/or conference presentations.
• The ability to be a self‑starter, to work independently, and to participate creatively in a collaborative team effort.
• Proven ability to function well in a dynamic research environment, set priorities, multi‑task, and adapt to ever‑changing needs.

For employment at Oak Ridge National Laboratory (ORNL), a Real  form of identification will be required.…