Postdoctoral Research Associate - Computational Solid Mechanics and AI/ML

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.

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.
• 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.