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

Requisition Id 15685

Overview:

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).

As a Postdoctoral Research Associate, you will contribute to research in these areas, bridging state-of-the-art atomistic and mesoscopic simulation methods as indicated above as well as nanoscale experiments with domain-informed AI/ML algorithms. In addition to fundamental science discovery, the research will pursue development of automated workflows and novel ML-approaches that allow integration of different theory, simulation, and experimental protocols. The research is designed to provide opportunities for development of your experience and scientific vision. The applicant will also work closely with scientists at CNMS as well as those involved in a multi-institution collaboration (~30 researchers) spanning Oak Ridge National Laboratory, Argonne National Laboratory, Northwestern University, and Lawrence Berkeley National Laboratory to address grand challenge problems in materials for next-generation microelectronics applications.

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.

Major Duties/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.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Basic 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.).

Preferred Qualifications:

• 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.

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

Letters of Recommendation:

Please submit three letters of reference when applying for this position. You can upload these directly to your application or have them sent to postdocrecruitment@ornl.gov with the position title and number referenced in the subject line.

Instructions to upload documents to your candidate profile:

• Login to your account via jobs.ornl.gov
• View Profile
• Under the My Documents section, select Add a Document

Security, Credentialing, and Eligibility Requirements:

• This position requires the ability to obtain and maintain an HSPD-12 PIV badge.
• For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required.
• Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.
• To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

• If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment.
• Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.

Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov.

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.