Postdoctoral Research Associate - Computational Solid Mechanics and AI/ML

Requisition Id 17416

Overview:

We are seeking a Postdoctoral Research Associate who will focus on theoretical and computational solid mechanics, including constitutive modeling, fracture and damage mechanics, and multiscale/multiphysics modeling of advanced materials and structural systems. This position resides in the Nuclear Infrastructure Deployment and Optimization (NIDO) Group, Nuclear Science and Engineering Division (NSED), Fusion and Fission Energy and Science Directorate (FFESD), at Oak Ridge National Laboratory (ORNL).

The position will focus on developing and applying advanced computational models for constitutive behavior, fracture, damage, degradation, and multiscale/multiphysics material behavior, with particular emphasis on heterogeneous and quasi-brittle materials such as concrete. Applications will include civil infrastructure, advanced construction technologies, nuclear structures, and other energy-related structural systems.

The research will also involve the integration of physics-based computational mechanics with artificial intelligence and machine learning (AI/ML), including physics-informed and hybrid modeling, surrogate modeling, and engineering data analytics.

Major Duties/Responsibilities:

• Conduct research in theoretical and computational solid mechanics, including constitutive modeling, fracture and damage mechanics, and nonlinear material behavior.
• Develop and apply mesoscale, multiscale, discrete, and finite element models for heterogeneous materials and structural systems.
• Develop coupled multiphysics models incorporating mechanical, thermal, transport, and environmental effects on material degradation and long-term performance.
• Implement and extend computational models using multiphysics finite-element frameworks such as MOOSE and discrete/mesoscale approaches such as the Lattice Discrete Particle Model (LDPM).
• Apply computational mechanics to evaluate structural performance, design margins, and the significance of construction deviations in civil and nuclear infrastructure.
• Develop and apply AI/ML methods for engineering applications, including physics-informed and hybrid modeling, surrogate models, and data analytics.
• Utilize experimental and characterization data to develop, calibrate, and validate comprehensive computational models.
• Perform large-scale simulations using high-performance computing platforms, including model verification, validation, and uncertainty assessment.
• Collaborate with computational researchers, experimental scientists, structural engineers, and other domain experts across ORNL.
• Publish research in peer-reviewed journals, prepare technical reports, and present results at conferences and technical 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 Civil Engineering, Mechanical Engineering, Engineering Mechanics, Materials Engineering, or a related discipline within the past five years.
• Strong background in solid mechanics, including constitutive modeling, fracture mechanics, damage mechanics, or nonlinear material behavior.
• Demonstrated experience in computational mechanics, including finite element, discrete, mesoscale, or multiscale modeling approaches.
• Experience developing or applying numerical models for material or structural behavior.
• Scientific programming experience using Python, C++, or similar languages.
• Experience with Linux-based computational environments and scientific software development tools.
• Excellent written and oral communication skills, with demonstrated ability to work independently and collaboratively in multidisciplinary research teams.

Preferred Qualifications

• Strong background in computational fracture and damage mechanics, constitutive modeling, and heterogeneous or quasi-brittle materials.
• Experience with LDPM, lattice/discrete models, mesoscale mechanics, or related computational approaches.
• Experience with multiphysics finite-element frameworks such as MOOSE and high-performance computing.
• Knowledge of coupled mechanical, thermal, transport, and environmental degradation processes.
• Experience with AI/ML for engineering applications, particularly physics-informed or hybrid modeling, surrogate models, and scientific data analytics.
• Demonstrated understanding of how experimental data can support the development, calibration, and validation of comprehensive computational models.
• Demonstrated knowledge of concrete structural behavior, design principles, and relevant codes and standards.
• Experience with civil infrastructure, nuclear structures, advanced construction, or other energy-related structural systems.
• Demonstrated research productivity through peer-reviewed publications and the ability to contribute effectively to multidisciplinary research projects.
• 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

• 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.
• Work involves various physical requirements and working conditions. As such, a pre-hire physical will be required after offer.
• 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.

Letters of Reference:

Please submit two letters of reference when applying to this position. You may 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

Relocation:

Moving can be overwhelming and expensive. UT-Battelle offers a generous relocation package to ease the transition process. Domestic and international relocation assistance is available for certain positions. If invited to interview, be sure to ask your Recruiter (Talent Acquisition Partner) for details.

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.

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.

If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.

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.