Postdoctoral Research Associate- AI​/ML Accelerated Theory Modeling & Simulation Microelectronics

Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics

life insurance, parental leave, 401(k), retirement plan, relocation assistance

The Center for Nanophase Materials Sciences (CNMS) is seeking a Postdoctoral Research Associate to develop novel AI/ML algorithms that incorporate multi-scale computational simulations to aid data fusion across experimental modalities. The focus is to discover new materials phenomena and develop physics-aware ML models that bridge length/time scales for improved mechanistic insights into nanomaterials response. The bulk of the work will center on novel materials for next-generation microelectronic devices such as oxide ferroelectrics and 2D memristive materials.

As a Postdoctoral Research Associate, you will contribute to research that bridges state-of-the-art atomistic and mesoscopic simulation methods and nanoscale experiments with domain-informed AI/ML algorithms. You will also develop automated workflows and novel ML approaches that integrate theory, simulation, and experimental protocols. The role offers opportunities to advance your scientific vision and collaborate with scientists at CNMS and a multi-institutional partnership spanning Oak Ridge National Laboratory, Argonne National Laboratory, Northwestern University, and Lawrence Berkeley National Laboratory.

The position resides in the Theory & Computation Section, Center for Nanophase Materials Sciences (CNMS), Physical Sciences Directorate (PSD) at ORNL and will be jointly supervised by Dr. P. Ganesh, Dr. Rama Vasudevan and Dr. Vitali Starchenko.

• Develop and validate AI/ML models for knowledge extraction (e.g., discovery of governing equations, correlative analysis across length/time scales) from multi-scale simulations and multi-modal experiments.
• Perform data fusion using novel AI/ML approaches to transfer information from simulations and experiments into data ingestion pipelines for model refinement.
• Conduct multi-scale simulations (e.g., DFT, atomistic, phase-field) to train AI/ML models.
• Conduct scientific research on ferroelectrics and/or 2D memristive materials.
• Create and maintain datasets in databases on in-house data storage resources, working closely with ORNL's workflow and data management scientists.
• Collaborate meaningfully with experimental groups involved in the project.
• Report and publish scientific results in peer-reviewed journals timely.
• Present results at international scientific conferences and meetings.
• Align with ORNL's core values of Impact, Integrity, Teamwork, Safety, and Service, promoting equal opportunity and a respectful workplace.

• A PhD in Physics, Materials Science, Chemistry, or a closely related field completed within the last 5 years.
• Sound understanding of advanced ML concepts and architectures and hands‑on experience with open-source AI/ML packages (e.g., PyTorch, scikit‑learn, Tensor Flow, JAX).

• Good grasp of solid‑state physics, ferroelectrics, and/or 2D materials.
• Strong background in developing or applying materials simulation methods such as atomistic simulations using electronic‑structure or machine‑learning interatomic potentials (MLIPs) and phase‑field modeling, particularly for next‑generation microelectronics (e.g., oxide ferroelectrics, 2D materials).
• Familiarity with AI/ML algorithms for generative materials design or knowledge extraction (e.g., causal ML or symbolic regression).
• Excellent coding experience for data analysis using Python, Julia, etc., with a keen interest in developing advanced AI/ML algorithms.
• Experience creating and/or working with computational databases using automated workflows.
• Record of productive and creative research with peer‑reviewed publications.
• Excellent written and oral communication skills.
• Motivated self‑starter capable of working independently and creatively in collaborative teams.
• Able to function well in a fast‑paced research environment, setting priorities to accomplish multiple tasks within deadlines.

Applicants must not have received their Ph.D. more than five years prior to the date of application and must…