Postdoctoral Research Associate

Postdoctoral Research Associate - AI-Accelerated Discovery of Permanent Magnets 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 – especially related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful system) and disordered materials is also desirable. The project will involve developing autonomous materials discovery workflows on HPC platforms that can learn structure-chemistry-property relationship in complex magnets via interpretable machine-learning models, and develop improved AI models that can accelerate prediction of new synthesizable magnet candidates with high energy density and critical temperatures based on these predictions. 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 include close interaction with experimental programs at MSTD to synthesize new permanent magnets. The candidate is expected to work closely with Addis Fuhr and P. Ganesh. As part of our research team, you will be working with a highly interdisciplinary team of scientists at the CNMS, MSTD and across other divisions at ORNL. Major Duties/Responsibilities: Work closely with members of NTI and CNMS to develop new AI models for discovering novel permanent magnets with targeted properties using advanced concepts such as classifier free guided diffusion models, transformers with multi-headed attention, physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression, reinforcement learning, monte-carlo tree-search, causal ML etc. Design, develop, and validate interpretable cross-modal AI/ML models incorporating features from electronic structure theory for predictive structure-chemistry-property discovery in magnetic solids and validate them against multi-modal experimental measurements Perform high-throughput first-principles electronic structure calculations (e.g. DFT and post-DFT methods) for generating datasets to train AI models leveraging DOE’s HPC platforms Develop new methodologies that can describe both atomic and spin relaxation accurately but at a much cheaper computational cost than DFT 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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success Basic Qualifications: A PhD in Condensed Matter Physics, Materials Science, Chemistry, Physics, or a closely related science discipline completed within the last five years Preferred Qualifications: 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 materials Some form of expertise in methods such as machine-learning force-fields for spinful materials, or multi-fidelity Bayesian models that can learn machine-learning force-fields along with effective spin Hamiltonians from ab initio / experimental dataset or machine-learning tight-binding DFT methods Expertise in using or developing generative 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 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 Special Requirements: Security, Credentialing, and Eligibility Requirements: 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. Postdocs: 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. 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. 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. 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. #J-18808-Ljbffr