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

About the role

The Center for Nanophase Materials Sciences (CNMS) is seeking a Postdoctoral Research Associate to support research directed towards developing novel AI/ML algorithms that can incorporate multi-scale computational simulations to aid with data fusion across multiple modalities of experiments with the final goal of discovering novel materials phenomena or even new materials. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics-models and/or physics-aware ML-models that can bridge length/time scales, to provide improved mechanistic insights into nanomaterials response. Bulk of the work will be on novel materials for next-generation microelectronic devices (e.g. oxide ferroelectrics and 2D memristive materials).

Responsibilities

• Develop and validate AI/ML models that can be used for knowledge extraction (e.g. discovery of governing equations; correlative analysis across length/time-scales etc.) from multi-scale simulations and multi-modal experiments.
• Perform data fusion using novel AI/ML approaches to seamlessly transfer information from simulations and experiments into data ingestion pipelines for model refinement.
• Perform multi-scale simulations (e.g. DFT / atomistic / phase-field simulations) 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.
• Meaningfully collaborate with experimental groups involved in the project.
• Report and publish scientific results in peer-reviewed journals in a timely manner.
• Present results at international scientific conferences and meetings.

Qualifications

• A PhD in Physics, Materials Science, Chemistry, or 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 (such as pytorch, scikit-learn, tensorflow, JAX etc.).
• Good grasp of concepts in solid-state physics, ferroelectrics and/or 2D materials.
• Strong background in developing and/or applying materials simulation methods, such as atomistic simulations using electronic-structure and/or machine-learning interatomic potentials (MLIPs) and phase field modeling, particularly related to materials for next-generation microelectronics (e.g. oxide ferroelectrics, 2D materials and related systems).
• Strong familiarity with AI/ML algorithms, for generative materials design, or for knowledge extraction, e.g. causal ML or symbolic regression, etc.
• Strong demonstrated background in coding for data analysis using Python, Julia etc. with knowledge or keen interest to develop and meaningfully incorporate advanced AI/ML algorithms to advance their research.
• Experience creating and/or working with computational databases using automated workflows.
• An excellent record of productive and creative research shown by a record of publications in peer-reviewed journals.
• Excellent written and oral communication skills.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.